Tomography doi: 10.3390/tomography12060083

Authors: Sennett Yang Jazza Jamil Diep Nguyen Hannah Murphy Emily Foldes Jacob J. Knittel Maddie Muenzer Clay M. Oliver Raza Mushtaq Justin L. Hoskin Matthew T. Borzage Kevin S. King

Background/Objectives: Normal pressure hydrocephalus (NPH) is a treatable cause of gait impairment and fall risk in older adults, yet it remains frequently underdiagnosed. This study aimed to validate an automated measurement of the callosal angle, a recognized imaging marker of NPH, adapted for use on routine head computed tomography (CT). Methods: We performed a retrospective analysis of 198 patients with probable NPH, confirmed by gait improvement following lumbar tap test, and 198 age- and sex-matched controls presenting with headache and negative head CT findings (mean age 74 ± 7 years; 60% male in both groups). Manual callosal angle measurements were independently obtained by trained residents and reviewed by neuroradiologists. Automated and manual measurements were compared using intraclass correlation, and diagnostic performance was assessed across threshold values. Results: Automated callosal angle measurements demonstrated strong agreement with manual measurements (ICC = 0.90). Using an automated callosal angle threshold of <90°, diagnostic accuracy was 84.1%, with sensitivity of 90.4% and specificity of 77.8%. Optimization to a 95° threshold yielded an accuracy of 85.9%, with both sensitivity and specificity of 85.9%. The area under the receiver operating characteristic curve was 0.915 (95% CI, 0.897–0.933). Conclusions: Automated callosal angle assessment on routine head CT provides reliable and scalable detection of NPH, supporting its use as a screening tool to facilitate earlier diagnosis and treatment of a potentially reversible cause of dementia.

Tomography doi: 10.3390/tomography12060082

Authors: Zahra H. M. Alquraish Yuki Arita Thomas C. Kwee

Purpose: To evaluate how often history taking and physical examination are omitted before MRI referral and whether their omission is associated with clinical reasoning quality and MRI diagnostic yield. Materials and Methods: In this prospective study, adults undergoing MRI at a tertiary academic hospital were surveyed before imaging to determine whether the referring clinician had taken their history and performed a physical examination. Multivariable regression was used to assess determinants of omission and associations with clinical reasoning quality (defined as agreement between the suspected diagnosis and MRI findings) and MRI positivity (defined as findings relevant to the indication). Results: Among 275 patients (median age 61 years; 50.0% male), history taking was omitted in 18.2% of cases and physical examination was omitted in 70.9%. History taking was less likely during surveillance than during new/first visits (odds ratio (OR) 0.140, p < 0.001) and more likely when MRI was requested by residents rather than medical specialists (OR 4.645, p = 0.018). Physical examination was more likely when MRI was requested by residents (OR 3.174, p = 0.007) or nurse specialists/physician assistants (OR 3.145, p = 0.033), and less likely during follow-up visits (OR 0.183, p < 0.001) and surveillance visits (OR 0.061, p < 0.001). Omission of physical examination was not associated with clinical reasoning quality (p = 0.370). Neither omission of history taking nor omission of physical examination was associated with MRI positivity (p = 0.430 and p = 0.286, respectively). Conclusions: History taking and physical examination were often omitted before MRI referral. Although no statistically significant association was observed between omission of bedside assessment and clinical reasoning quality or MRI positivity, reduced bedside assessment may limit the clinical context informing referral and interpretation.

Tomography doi: 10.3390/tomography12060081

Authors: Felix Waßmer Rouven Bauer Stefan O. Schoenberg Alexander Hertel Isabelle Ayx

Background/Objectives: Photon-counting computed tomography (PCCT) combined with radiomics enables advanced myocardial tissue characterization beyond conventional imaging. This study investigated whether myocardial radiomic features derived from PCCT are associated with nicotine status in patients without coronary artery disease. Methods: In this retrospective, single-center study, 104 patients (38 men, 66 women; median age 54 years) without coronary calcification (Agatston score = 0) underwent cardiac PCCT. Myocardial septal thickness was measured at three points during the 65–70% cardiac phase. Myocardial tissue was manually segmented, and 105 radiomic features were extracted. After correlation-based feature reduction, 45 independent features were used for analysis. Patients were categorized based on nicotine status. Machine learning models, including logistic regression, random forest, and gradient boosting, were trained and evaluated using stratified five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC) and additional classification metrics. Results: No significant differences in myocardial septal thickness were observed between smokers and non-smokers (p > 0.05). However, radiomic features enabled moderate discrimination between smokers and non-smokers. Logistic regression with L2 regularization achieved the best performance (ROC-AUC 0.66, balanced accuracy 0.67), outperforming random forest and gradient boosting models. The most relevant radiomic features primarily comprised higher-order texture and shape-based parameters associated with spatial gray-level heterogeneity and subtle variations in myocardial tissue architecture. Conclusions: PCCT-based radiomics may capture subtle myocardial imaging signatures associated with smoking status, even in the absence of structural changes detectable by conventional metrics. These findings highlight the potential of cardiac radiomics as a non-invasive imaging biomarker for early cardiovascular risk assessment and support its integration into advanced cardiac imaging workflows. Future multicenter studies with larger cohorts, external validation, and multimodal correlation are warranted to improve robustness and facilitate clinical translation.

Tomography doi: 10.3390/tomography12060080

Authors: Yujia Sun Yingying Yang Nan Bao

Background: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) interpolation and segmentation are critical for clinical diagnosis, anatomical quantification and personalized treatment. Most existing methods perform these two tasks separately, leading to computational redundancy and insufficient mining of shared spatial features. This study aims to construct an integrated multi-task learning framework for the synchronous processing of medical image interpolation and segmentation. Methods: We propose a unified multi-task framework named TASC-SwinMT for joint interpolation and multi-frame segmentation of CT and MRI images. It employs a shared SwinUNet encoder to extract general spatial features, matched with two task-specific decoders for frame prediction and mask generation. Three functional modules are designed for cross-task synergistic learning, and a dynamic multi-task loss function is used to balance objective optimization. Experiments are performed on Medical Segmentation Decathlon Task02_Heart and Task06_Lung datasets. Results: Our method outperforms baseline models and ablation variants in both tasks with outstanding accuracy and significantly reduced computational overhead. It exhibits superior performance in lesion boundary depiction, small object segmentation and inter-slice consistency, and anatomical prior constraints with frequency-domain modeling further enhance prediction quality. Conclusions: The cross-task feature sharing and joint optimization strategy are validated effective. The proposed TASC-SwinMT framework has favorable stability and generalization ability, providing a reliable solution for clinical medical image analysis.

Tomography doi: 10.3390/tomography12060079

Authors: Ye Jun Park Eun Ju Yoon Jun Hyung Hong Eai Hong Hwang Tae-Hoon Kim Seong-Jung Kim Soo-Min Heo Hyun Chul Kim Sang Gook Song Jin Woong Kim

Objectives: To investigate whether hepatic capsular thickness (HCT) measured on ultrasonography (US) is associated with HCT measured on arterial-phase computed tomography (CT), and to evaluate the potential discriminative performance of US-measured HCT in women with Fitz–Hugh–Curtis syndrome (FHCS). Methods: In this retrospective dual-center case–control study, 17 women with clinically diagnosed FHCS who underwent both arterial-phase CT and abdominal US within a 3-day interval were included. Thirty-five healthy women served as controls. HCT was measured on CT and US by two abdominal radiologists blinded to clinical information. HCT values were compared between groups, the association between CT and US measurements was assessed, interobserver agreement was evaluated using the intraclass correlation coefficient (ICC), and receiver operating characteristic analysis was performed to explore candidate cutoff values for discriminating FHCS from controls. Results: Median HCT on CT was significantly greater in the FHCS group than in the control group [1.80 mm (IQR, 1.60–2.00) vs. 0.60 mm (IQR, 0.40–0.70); U = 595.0, p < 0.001]. Median HCT on US was also significantly greater in the FHCS group than in the control group [1.50 mm (IQR, 1.30–2.00) vs. 0.70 mm (IQR, 0.60–0.80); U = 589.0, p < 0.001]. CT- and US-based HCT measurements showed a significant positive correlation (rho = 0.66, p < 0.001). Interobserver agreement for HCT measurement was good in the overall cohort (ICC, 0.804; 95% confidence interval [CI], 0.66–0.89). In exploratory receiver operating characteristic (ROC) analysis, the candidate cutoff values were 1.1 mm for CT and 0.85 mm for US. These ROC-derived metrics should be interpreted as exploratory estimates from an idealized case–control setting rather than as real-world diagnostic performance. Conclusions: US-measured HCT was significantly increased in women with clinically diagnosed FHCS and showed a significant positive correlation of moderate strength with CT-measured HCT. These findings suggest that US-based HCT assessment may provide supportive imaging information in patients with suspected FHCS. Further validation in larger cohorts, particularly in clinically relevant control populations, is warranted.

Tomography doi: 10.3390/tomography12060078

Authors: Isaac E. Prentiss Sasha Hakhu Jennapher Lingo VanGilder Parvathy Hareesh Andrew Hooyman Jason Yalim Justin Hines Gabe LaFond Edward Ofori Leslie C. Baxter Yuxiang Zhou Leland S. Hu Kurt G. Schilling Scott C. Beeman

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T1-weighted, T2-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T1/T2-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P2 (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P2 values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P2 was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema.

Tomography doi: 10.3390/tomography12060077

Authors: Shrooq T. Aldahery Lubna A. Bushara Rana A. Alasami Mona H. Alqurashi Rahaf O. Alqurayqiri Sahar E. Behilak Faten S. Kandil Khalid M. Alshamrani Walaa M. Alsharif Awadia Gareeballah Fahad H. Alhazmi Mohammed S. Almatrafi

Purpose: This study aimed to assess MRI-related claustrophobia severity and patient-reported experiences among Saudi patients to examine their associations with selected demographic variables. Methodology: A cross-sectional study was conducted using a structured questionnaire administered to 200 Saudi patients who had previously undergone MRI examinations. The questionnaire comprised five sections covering demographic data, phobia severity and patient-reported experiences before, during and after MRI examinations. Statistical analysis was performed using SPSS statistical package (IBM SPSS Statistics version 26, IBM Corp., Armonk, NY, USA), applying chi-square tests to examine associations between demographic variables and questionnaire responses. Results: A significant majority of participants, 76.5%, reported a positive MRI experience, whereas only 6.5% reported a negative experience. Shortness of breath during the MRI examination was the most frequently reported source of discomfort (75%). Significant associations were identified between demographic characteristics and phobia severity. Age and gender were significantly correlated with sudden fear responses, while educational level was strongly associated with receiving adequate pre-scan information and overall examination experience. Conclusions: Despite the high percentage of positive experiences, a notable proportion of participants reported anxiety-related distress during MRI examinations. The observed associations between demographic variables and claustrophobia-related responses suggest the potential value of patient-centred approaches, particularly improved pre-scan education, to improve the MRI-related patient experience and reduce anxiety-related distress.

Tomography doi: 10.3390/tomography12060076

Authors: Elif Yusufoğlu Salih Taha Alperen Özçelik Orhan Atila Numan Halit Guldemir Abdulkadir Sengur

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment.

Tomography doi: 10.3390/tomography12050075

Authors: Amalia Lupi Sebastiano Gambato Ambra Checchetto Stefania Zinato Sophie Mavrogeni Filippo Crimì Marco Castellaro Emilio Quaia Alessia Pepe

Purpose: Myocardial iron overload has been demonstrated to have a heterogeneous distribution. A segmental T2* CMR approach, with correction factors applied to account for artifacts, has been demonstrated to be feasible and has permitted a reduction in cardiac morbidity and mortality, by better capturing the heterogeneous distribution of myocardial iron overload. To the best of our knowledge, commercially available software does not provide a segmental T2* technique. Our aims were to prospectively examine a large population of healthy volunteers, stratified by sex and age, using the Black Blood MEGE T2* mapping technique, to obtain normative values of the myocardium, to assess their relationship with physiological variables, and to fix correction factors for a segmental approach by using a commercially available software. Methods: Fifty healthy subjects (M:F = 1:1, 20–69 years) underwent CMR without a contrast agent. Segmental T2* values were obtained using cvi42 software; global values were the mean. Inter-study, and intra- and inter-operator reproducibility were assessed to confirm the stability of the acquired data. The association of T2* values with physiological characteristics, and myocardial wall thickness were assessed. The fluctuation of all segments versus the mid-septum was calculated to obtain a correction factor for each segment for the software used. Regional T2* differences were examined. A p-value <0.05 was considered statistically significant. Results: Twenty-five males and females, five for each decade (mean age 43 ± 13.8 years), were included. The native T2* values in all subjects averaged at 34.03 ± 6.65 ms (range 29.9–37.9 ms). Reproducibility analyses showed good correlations between the various datasets (ICC > 0.80). A weakly negative correlation was observed between age and T2* (p = 0.04). Segmental correction factors were developed and found to be significantly different from correction factors developed by non-commercially available software on non-state-of-the-art technology for sequences and scanners. Conclusions: Age-specific normative values and higher normal cut-off values than the conservative 20 ms are recommended to avoid systematic biases in the identification of pathological findings. Moreover, the correction factors developed by using the most reproducible Black Blood MEGE sequences and a commercially available software on a scanner of the current era could be a significant step toward spreading a more sensitive T2* segmental approach in the clinical arena worldwide.

Tomography doi: 10.3390/tomography12050074

Authors: Jingjing Feng Zongli Jiang Jinli Zhang

Background/Objectives: Early and accurate prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is vital for personalized breast cancer treatment. However, existing deep learning methods are hampered by tumor heterogeneity and semantic misalignment between high-dimensional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and low-dimensional clinical data, which limits pCR prediction performance and generalization. This study addresses these challenges via a novel multimodal network. Methods: We propose a Bidirectional Perceptual Multimodal Interaction Network (BPMINet) based on contrastive learning. BPMINet integrates pre-NAC DCE-MRI and clinical information through three core components: (1) we propose a bidirectional cross-modal attention (BiCMA) fusion mechanism to resolve semantic misalignment and facilitate effective multimodal feature fusion; (2) we design a multimodal contrast-aware feature enhancement (MCFE) module as a key component tightly integrated into the pCR-oriented contrastive learning framework, which serves to boost discriminative power for pCR prediction and improve generalization performance on hard-to-classify samples; (3) we adopt a dual-loss strategy to enable the collaborative optimization of discriminative feature representation and pCR prediction performance. Results: On two publicly available multicenter datasets, BPMINet outperformed all comparative methods across seven evaluation metrics: specifically, it surpassed the top-performing baseline by 5.17% in AUC and 5.24% in accuracy on the MAMA-MIA dataset. More notably, it achieved substantially larger gains of 11.72% in AUC and 7.38% in accuracy on the ISPY1 dataset. Conclusions: BPMINet achieves optimal pCR prediction performance, confirming its superiority and strong generalization ability for multimodal breast cancer pCR prediction.

Tomography doi: 10.3390/tomography12050073

Authors: Miki Yoshimura Yuki Hashimoto Yuko Kodama Aris Hatanaka Ryusei Yakushiji Shiho Ikeda Nazuna Inoue Maho Wakabayashi Ichika Kawazu Takeshi Yoshitomi

Objectives: This study investigated whether visual field abnormalities are present in eyes with suspected nasal optic disc hypoplasia (NOH) by using fundus photography and optical coherence tomography (OCT). Methods: NOH was diagnosed using the following criteria: (1) small optic disc, (2) nasal optic disc pallor or optic disc margin irregularity, (3) wedge-shaped temporal visual field defects extending from Mariotte’s blind spot, and (4) reduced nasal circumpapillary retinal nerve fiber layer (cpRNFL) thickness. Eyes fulfilling criteria 1, 2, and 4 without visual field abnormalities were classified as pseudo-NOH (pNOH), whereas eyes without visual field or cpRNFL abnormalities were considered normal. Nasal cpRNFL thickness was measured using OCT, radial peripapillary capillary (RPC) density was assessed using OCT angiography (OCTA), visual field testing was performed, and optic disc blood flow velocity was evaluated using the mean blur rate (MBR) and laser speckle flowgraphy (LSFG). Results: Seven eyes with NOH, 13 eyes with pNOH, and 24 normal right eyes were included. Nasal cpRNFL thickness and MBR were significantly reduced in both the NOH and pNOH groups compared with the normal group, with no significant difference between the NOH and pNOH groups. Nasal RPC density was significantly lower in the NOH group than in both the pNOH and normal groups, and no significant difference was observed between the pNOH and normal groups. Conclusions: Even when NOH was suspected from fundus, LSFG, and OCT C-scan findings, visual field abnormalities were not consistently present. Differences in RPC density measured using OCTA may have contributed to this variability. This study examined whether suspected nasal optic disc hypoplasia (NOH) is always associated with visual field defects. Using fundus imaging, OCT, OCT angiography, and laser speckle flowgraphy, we compared eyes with NOH, pseudo-NOH, and normal eyes. Although structural changes such as reduced nasal nerve fiber layer thickness and decreased blood flow were observed in both NOH and pseudo-NOH, visual field abnormalities were not consistently present. Notably, reduced radial peripapillary capillary density was specific to NOH, suggesting that vascular differences may explain variability in visual function. These findings highlight the importance of multimodal imaging in NOH evaluation.

Tomography doi: 10.3390/tomography12050072

Authors: Ji-Le Jiang Teng-Hui Ge Zhong-Ning Xu Jing-Ye Wu Yu-Qing Sun

Background: Cage subsidence (CS) is among the main complications after oblique lumbar interbody fusion (OLIF) and may lead to the failure of indirect decompression. Accurate preoperative bone quality assessment is critical for risk stratification, yet the optimal imaging modality and diagnostic threshold remain unclear. Objectives: This study aimed to determine a quantitative computed tomography (QCT)-derived volumetric bone mineral density (vBMD) threshold for predicting CS after OLIF with posterior fixation. Methods: Patients undergoing OLIF with posterior fixation between July 2017 and March 2020 were retrospectively enrolled. Preoperative vBMD was measured using QCT as the average L2–L4 trabecular volumetric BMD. CS was defined as a loss of more than 2 mm of disk height on sagittal midline CT views between 3 days postoperatively and the last follow-up. Clinical and radiographic parameters including gender, age, body mass index, vBMD, number of operative levels, cage dimensions, disk height, segmental lordosis, intraoperative endplate injury, and fusion status were analyzed. Results: 86 patients (107 operative levels) with a mean follow-up of 20.6 months were included; 25 levels (23.4%) developed CS. Multivariate logistic regression identified vBMD (p < 0.001; OR 0.947; 95% CI 0.923–0.972) and intraoperative endplate injury (p = 0.031; OR 3.640; 95% CI 1.125–11.776) as independent risk factors. The area under the receiver operating characteristic curve (AUC) for vBMD was 0.847 (95% CI, 0.762–0.932), with an optimal threshold of 83.0 mg/cm3 (sensitivity 84.0%, specificity 76.8%). This threshold closely aligns with the American College of Radiology QCT criterion for osteoporosis (80 mg/cm3); however, given that it was derived from a single-center retrospective cohort, external validation in multi-center studies is warranted before broad clinical adoption. Fusion rates differed significantly between CS and non-CS groups (84.0% vs. 96.3%, p = 0.029). Conclusions: QCT-derived vBMD provides a phantom-calibrated, protocol-standardized metric for preoperative risk stratification of cage subsidence after OLIF.

Tomography doi: 10.3390/tomography12050071

Authors: Minna Lerner Patrik Brynolfsson Filip Szczepankiewicz Joakim Medin Pia C. Sundgren Lars E. Olsson Sara Alkner

Objectives: Early identification of treatment response in brain metastases remains clinically challenging. This study explores tensor-valued diffusion MRI (dMRI), specifically q-space trajectory imaging (QTI), as a novel source of early imaging biomarkers during stereotactic radiotherapy (SRT). Methods: Twenty-six patients with brain metastases were enrolled; thirteen met quality and completeness criteria for QTI analysis (10 responders, three non-responders). MRI was acquired at four time points: before SRT, before final SRT fraction, and at 3 and 6 months post-SRT. QTI-derived metrics included mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (µFA), and isotropic (MKI) and anisotropic (MKA) diffusional variance. Parameter values within the tumour volume were compared pre- and during SRT and correlated with treatment response from standard MRI follow-up. Overall survival was assessed using Kaplan–Meier analysis. Results: Median survival was 12 months. QTI analysis was feasible with visible changes in the tumour tissue parameter maps over time. Statistically significant differences (p < 0.05) were found between responders and non-responders in FA before treatment. MKI in responders was significantly lower (p < 0.05) during SRT than before. Conclusions: This study presents a first exploration of QTI-derived parameters in a cohort of patients with brain metastases. We demonstrate feasibility and a scalable workflow, supporting further investigation in larger cohorts and in patients with larger or more stable lesions.

Tomography doi: 10.3390/tomography12050070

Authors: El-Sayed H. Ibrahim Slade Klawikowski Lindsay Puckett Elizabeth Gore Dayeong An Jakub Bychowski Antonio Sosa Gerard Walls Carmen Bergom

Background/Objectives: Magnetic resonance imaging (MRI) provides a non-invasive means for a comprehensive assessment of the effect of radiation therapy (RT) on heart function. This study aims to determine RT induced cardiotoxicity in thoracic cancer patients using cardiac MRI. Methods: Cardiac MRI was performed at baseline and at six months post-treatment in patients undergoing standard-of-care RT for lung or esophageal cancers at a single institution. Parameters included regional myocardial strain in the longitudinal, circumferential, and radial directions as well as myocardium T1, T2, and extracellular-volume (ECV) maps. Cardiac segmental doses were extracted from the RT planning scans. The relationship between changes in segmental MRI parameters at six months and segmental heart RT dose were investigated. Results: Twelve patients underwent baseline MRI and four completed the follow-up MRI. Five of the segmental strain parameters showed notable changes between baseline and six-month follow-up. Increased doses in the heart base and apex were associated with moderate-to-large and mild deteriorations, respectively, in strain for all regions. Increased doses in the mid-ventricular regions were associated with improved strain in all regions. The segmental analysis revealed that myocardial regions nurtured by the left coronary artery are more negatively affected by radiation compared to those nurtured by the right coronary artery. Conclusions: Alterations in regional tissue and strain parameters on MRI vary according to local myocardial RT dose, suggesting there may be heterogeneity of radiation sensitivity for the heart substructures and regions. Changes in segmental strain parameters may reflect post-RT cardiac remodeling, but larger confirmatory studies are required.

Tomography doi: 10.3390/tomography12050069

Authors: Kenji Sadamatsu Kazumasa Kurogi Yasuhiro Nakano Takashi Kajiya

Coronary artery calcification, present in 20–30% of percutaneous coronary interventions (PCI), significantly impairs procedural success. Conventional angiography detects calcification in fewer than half of affected cases, while intravascular imaging—though precise—requires lesion crossability that cannot be guaranteed in up to 20% of severely calcified lesions. Cardiac CT (CCT) addresses both constraints by providing comprehensive, three-dimensional calcium characterization before the procedure begins, independent of wire crossability. This review details how specific CCT-derived parameters translate into procedural decisions. Calcium arc, depth, density, and longitudinal distribution each carry distinct implications for device selection: superficial high-density calcium favors atherectomy, while deep concentric patterns are better addressed by intravascular lithotripsy. Validated scoring systems—including the ABCD score—enable objective pre-procedural risk stratification. For chronic total occlusions, bifurcation lesions, ostial stenoses, and very long calcified segments, CCT provides lesion-specific information that supports stepwise strategy selection, equipment preparation, and anticipation of combined modification approaches. Importantly, CCT also identifies anatomical configurations—such as left main bifurcations or tortuous calcified segments—where specific device-related risks warrant particular caution. CCT and intravascular imaging serve complementary roles: CCT defines the strategic framework before the procedure, while intravascular imaging guides real-time execution and optimization. Limitations include operator-dependent interpretation, the absence of standardized protocols for translating calcium morphology into device selection, and the need to validate established Hounsfield unit thresholds in emerging photon-counting CT systems. Prospective randomized evidence comparing CCT-guided and intravascular imaging-guided strategies remains limited but is anticipated from ongoing trials.

Tomography doi: 10.3390/tomography12050068

Authors: Tommasa Catania Grazia Morabito Simone Barbera Massimo Venturini Federico Fontana Eduardo Maccarrone Grazia Maria Arillotta Velio Ascenti Silvio Mazziotti Thomas Joseph Vogl Giovanni Foti Tommaso D’Angelo Giorgio Ascenti

Background: Differentiating adrenal adenomas from non-adenomatous lesions remains a critical challenge in the management of adrenal incidentalomas. Conventional unenhanced CT relies on attenuation thresholds of 10 HU and 20 HU, which present trade-offs between sensitivity and specificity. Objectives: To evaluate the diagnostic performance of unenhanced Spectral CT using the attenuation difference between 40 keV and 140 keV virtual monoenergetic images for differentiating adrenal adenomas from non-adenomatous lesions. Methods: In this retrospective single-center study, 60 patients with adrenal lesions who underwent unenhanced dual-energy CT were included. Mean attenuation values were measured on conventional images and on virtual monoenergetic images at 40 keV and 140 keV. The spectral attenuation difference (Δ40–140 keV) was calculated. ROC analysis was performed to determine the optimal threshold and diagnostic performance. Additional analyses included DeLong comparison of correlated ROC curves and bootstrap resampling to estimate 95% confidence intervals for the area under the curve. Results: Forty-nine lesions were adenomas and eleven were non-adenomatous. The optimal threshold for Δ40–140 keV was −17 HU. When evaluated as a continuous variable, Δ40–140 keV yielded an area under the curve of 0.940 (95% confidence interval: 0.851–1.000), compared with 0.939 (95% confidence interval: 0.870–0.992) for conventional unenhanced attenuation. DeLong comparison showed no statistically significant difference between the two curves (p = 0.980). Diagnostic performance was as follows: HU ≤ 10 (AUC 0.816, diagnostic accuracy 0.70), HU ≤ 20 (AUC 0.883, diagnostic accuracy 0.87), and Δ40–140 keV ≤ −17 HU (AUC 0.940, diagnostic accuracy 0.90). The spectral attenuation difference demonstrated the highest overall diagnostic accuracy. Conclusions: Unenhanced Spectral CT using Δ40–140 keV improves discrimination between adrenal adenomas and non-adenomatous lesions compared with conventional attenuation thresholds. This technique may reduce indeterminate findings and limit the need for additional imaging.

Tomography doi: 10.3390/tomography12050067

Authors: Omer Yavuz Reyhan Ertan Muhammet Kertmen Mehlika Iscan

Background: Computed tomography (CT) is routinely used to estimate tumor size before lung resection, whereas pathologic examination provides the reference tissue-based measurement after surgery. This study aimed to compare CT-derived and pathologic tumor size and to evaluate correlation, agreement, proportional bias, clinically defined accuracy, and size-based T-category concordance, with particular attention to the effect of ground-glass opacity (GGO). Methods: This retrospective single-center study included 96 patients who underwent lung resection between January 2023 and December 2025 and had complete preoperative CT and pathologic tumor measurements. Maximum tumor diameter was defined as the largest of three orthogonal measurements for each modality. Correlation was assessed using Spearman’s rank correlation coefficient, reliability using the intraclass correlation coefficient (ICC), and agreement using Bland–Altman analysis. Proportional bias was evaluated by regression of the paired difference on the paired mean. Subgroup, size category, regression and size-based T-category concordance analyses were also performed. Results: CT and pathologic maximum diameters showed strong correlation (Spearman’s ρ = 0.952, p < 0.0001) and excellent reliability (ICC = 0.959, 95% CI, 0.939–0.973). The paired comparison was not statistically significant (p = 0.175), and the mean bias was −0.76 mm. However, the 95% limits of agreement ranged from −13.66 mm to +12.13 mm. Significant proportional bias was observed, with increasing CT underestimation as tumor size increased (slope = −0.093, p = 0.0014). In tumors with GGO, CT pathology differences shifted toward overestimation (+8.91 ± 7.30 mm vs. −1.64 ± 5.80 mm without GGO; p = 0.0003). Accuracy within ±5 mm and ±10 mm was 68.8% and 88.5%, respectively, but was lower in the GGO subgroup. CT-derived and pathology-derived size-based T-categories were concordant in 60 patients (62.5%), while pathology-based upstaging occurred in 23 patients (24.0%) and pathology-based downstaging in 13 patients (13.5%). Conclusions: CT-based tumor size showed strong overall correlation with pathologic measurements, but agreement at the individual patient level was more limited than correlation metrics alone would suggest. GGO and tumor size appeared to be important modifiers of measurement performance; however, the GGO-related findings should be interpreted cautiously because of the small subgroup size. These findings support cautious interpretation of CT-derived whole-lesion diameter, particularly in subsolid tumors and larger lesions.

Tomography doi: 10.3390/tomography12050066

Authors: Feifei Li Keith M. Furutani Chris J. Beltran

The sharp dose gradients that underpin the dosimetric advantage of particle therapy over photon therapy can be undermined by the interplay effects due to intra-fraction motion in modern pencil beam scanning systems. Fluoroscopy-Guided Particle Therapy (FGPT) offers a promising path to improved motion management through real-time tracking of tumors or surrogate signals. The advent of flat-panel detector (FPD)-based technology has enabled tighter integration of fluoroscopy/fluorography into treatment units and accelerated clinical adoption and research, with commercial systems such as Hitachi’s Real-time Gated Particle Therapy (RGPT) now available. However, the need for implanted fiducial markers, with the associated invasiveness and risk of complications, limits the utility of RGPT to a few anatomic sites in selected patients. The full potential of FGPT, therefore, depends on reliable marker-less tumor tracking, which remains challenging because soft-tissue targets are obscured by overlapping anatomy along the X-ray path, leading to reduced reliability of traditional image-registration algorithms in the projection domain. Recent advances in deep learning and AI-driven image registration have renewed hope for overcoming these barriers, enabling real-time marker-less tracking for particle therapy. This review outlines the evolution of fluoroscopy technology from image intensifier (II) to FPD-based systems, summarizes historical and recent vendor-supported FGPT strategies, and surveys emerging AI-based algorithms in the literature. A general review of machine learning-based image registration is provided, challenges in generalizability and interpretability are highlighted, and potential paths toward reliable, clinically deployable FGPT are discussed.

Tomography doi: 10.3390/tomography12050065

Authors: Julius Juhyun Chung Tianwen Ma Phaethon Philbrook Toby Zhou Adam Ezra Goldman-Yassen Phillip Zhe Sun

Background/Objectives: Accurate grading of brain gliomas is important, and amide proton transfer-weighted (APTw) MRI shows promise for non-invasive tumor differentiation. This study aimed to perform a comprehensive review and meta-analyses to demonstrate heterogeneity in both the diagnostic accuracy and quantitative consistency of APTw MRI in distinguishing high-grade gliomas (HGGs) from low-grade gliomas (LGGs), highlight issues with reporting standards and identify sources of heterogeneity through meta-regression. Methods: A systematic literature search was conducted between 1 January 2013 and 18 January 2026, following PRISMA guidelines. Peer-reviewed articles in English reporting diagnostic accuracy/contrast values of APTw MRI and study parameters were included. Principal component analysis (PCA) was used to extract the principal components (PCs) of the chemical exchange saturation transfer (CEST) contrast mechanism. Random-effects meta-analyses and univariate meta-regression models using individual CEST parameters and three PCs were performed. Forest plots with pooled estimates were generated. Leave-one-out meta-analysis (LOOMA) and complete case analysis were performed to examine the effects of outliers and missing data, respectively. Results: A total of 31 studies were included. Meta-analyses of the AUC and mean difference demonstrated significant heterogeneity across the studies (I2 = 73.9% & 78.2%, p < 0.001). The mean difference was moderated by one SD within the mean of the readout PC (p = 0.034) and the total PC (p = 0.02). The heterogeneity for the AUC and group mean difference was not substantially reduced by moderating nor LOOMA. The results of the meta-regression using all the data were similar to those using only data with no missing parameters. Conclusions: While APTw MRI shows promise for non-invasively distinguishing glioma grades, substantial heterogeneity in the study parameters limits generalizability. To improve consistency and comparability across studies, full reports of imaging parameters and standardization of APTw protocols are essential.

Tomography doi: 10.3390/tomography12050064

Authors: Alzahra Altalib Chunhui Li Alessandro Perelli

Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty. Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed? Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality, supervision strategy, and evaluation metrics, limiting the strength of direct comparative claims. The reviewed literature suggests that conditional diffusion models are a promising direction for CBCT-to-CT synthesis, but stronger dose-aware validation, standardized reporting, and broader multicenter evaluation are still needed before routine clinical deployment. This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42024619240.

Tomography doi: 10.3390/tomography12050063

Authors: James Hao Wang Meltem Uyanik Xue Li Weijie Chen Zhijin He Caitlin Randell Alan McMillan

Pharmacokinetic modeling in Positron Emission Tomography (PET) imaging has become a cornerstone in cancer research, offering insights into tumor development and progression. These models facilitate the quantification of radiotracer distribution and metabolism, enabling precise measurement of physiological parameters essential for cancer diagnosis, staging, and treatment monitoring. However, accurate pharmacokinetic modeling depends on reliable input function acquisition and partial volume correction techniques to minimize biases in quantitative PET metrics. This review provides a comprehensive overview of current methodologies and advancements in pharmacokinetic modeling for PET oncology imaging. We discuss techniques for acquiring input functions, including arterial, venous, and image-derived input functions (IDIFs), along with population-based input functions (PBIFs). Their strengths, limitations, and clinical applications are critically evaluated. Additionally, we examine quantitative methods such as partial volume correction (PVC) that mitigate the spatial resolution limitations of PET, improving radiotracer quantification in small or heterogeneous tumors. Furthermore, we explore advanced kinetic modeling techniques, including compartmental models, graphical approaches, and data-driven methods, highlighting recent innovations such as machine learning and Bayesian modeling. Key areas for future research in PET pharmacokinetic modeling include integrating hybrid imaging modalities, developing robust patient-specific input functions, and leveraging machine learning to streamline modeling processes. These advancements aim to enhance the precision and clinical utility of PET imaging in oncology, leading to more personalized cancer treatment strategies.

Tomography doi: 10.3390/tomography12050062

Authors: Shahd Aljoudi Aasiya Khan Iman Dajani Minatullah Al-Ani Michael Mina Dounia Baroudi Sama Al-Saffar Souha Aouadi Tarraf Torfeh Rabih Hammoud Noora Al Hammadi Mohammad S. Yousef

Background/Objectives: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men globally. Radiation oncologists often find PCa tumor characterization and outcome prediction challenging. Therefore, the potential for artificial intelligence (AI) implementation in radiation oncology has increased in recent years. This systematic review aims to evaluate the efficacy of AI algorithms in characterizing PCa tumors and predicting post-therapy outcomes. Methods: A total of 2055 studies were identified through a comprehensive search across PubMed and Scopus, then exported to Covidence. Inclusion criteria focused on prospective and retrospective cohort studies as well as randomized clinical trials (RCTs) published between 2015 and 2024 that explored the implementation of AI in tumor characterization and outcome prediction of PCa. Two independent reviewers evaluated each paper, and evaluation metrics such as specificity, sensitivity, accuracy, and area under the curve (AUC) were analyzed. The Risk of Bias in Non-randomized Studies of Interventions, Version 2 (ROBINS-I V2) tool was used to assess the risk of bias (ROB). Results: Across the 19 studies analyzed, there was no significant difference in model performance between machine learning (ML) and deep learning (DL) models. AI models using multi-input strategies (e.g., radiomics with clinical markers) generally performed better than single-input models. Of the imaging modalities used for radiomic feature extraction, multiparametric MRI (mpMRI)-trained AI models consistently achieved the highest performance. Conclusions: AI displays considerable potential for integration into clinical workflows for PCa management. However, further studies utilizing larger datasets and external cohorts independent of the sample population are needed to validate clinical utility and improve model transparency for reliable implementation.

Tomography doi: 10.3390/tomography12050061

Authors: Zuhal Y. Hamd Mohamed Abuzaid Mohamed Alharbi Nissren Tamam Amal I. Alorainy Lena Alrujaee Najla Almutairi Aljouharah Abdullah Alyagoub

Background and aim: Chest CT is a major contributor to population radiation exposure. Conventional, pooled diagnostic reference levels (DRLs) do not account for inter-individual variability in body habitus and are typically used retrospectively. We evaluated dose behavior in adult chest CT, derived BMI-stratified local DRLs, and developed models to enable AI-assisted, prescan dose prediction. Methods: Consecutive adult chest CT examinations from a single center were analyzed. Dose indices (CTDIvol, DLP) and patient factors (BMI, weight, height, age, sex; scan length and planned technical parameters where available) were extracted. DRLs were defined as the 75th percentile overall and within BMI categories (underweight, normal, overweight, and obese). Group differences were assessed using non-parametric tests; associations were examined using correlation analysis. Supervised learning (e.g., Random Forest, Gradient Boosting) was trained to predict CTDIvol and DLP from routinely available variables. Results: BMI-stratified DRLs increased monotonically with habitus: underweight 444.95 mGy·cm/9.60 mGy; normal 513.00/11.55; overweight 756.08/14.65; obese 931.60/20.25 (DLP/CTDIvol). Differences across BMI groups were significant for DLP (H = 31.53, p < 0.001) and CTDIvol (H = 33.61, p < 0.001). DLP correlated moderately with weight and BMI (r ≈ 0.54–0.56, p < 0.001), with a weaker association for age; height was not a meaningful predictor. No sex-based differences in CTDIvol or DLP were observed. Predictive models estimated CTDIvol and DLP with high performance (R2 up to ~0.79 and ~0.77, respectively), enabling comparison of predicted dose against BMI-matched DRLs before acquisition. Conclusions: Size-aware, BMI-stratified DRLs provide clinically interpretable investigation levels that avoid pitfalls of pooled benchmarks. Coupled with robust prediction of individualized dose from routine variables, this framework supports a shift from retrospective audit to prospective, point-of-care dose governance and protocol optimization in chest CT.

Tomography doi: 10.3390/tomography12050060

Authors: Christopher McDonough John Chrisekos Matthew Jurj Alycen Wiacek Alexey Tonyushkin

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0 μg of iron, below the estimated 4.8 μg sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications.

Tomography doi: 10.3390/tomography12050059

Authors: Tianxi Liang Rian Atri Sarah Joseph Yiyuan Shao Zhitong Zou Adrian J. Villanueva Aida Y. Prince Renke Deng Kurt Teichman Xinzi He Martin R. Prince

Background: As muscles atrophy, myocytes are replaced by fat and muscle strength diminishes, increasing thoracic supine kyphosis. Here, we investigate the relationship between muscle fat percentage (muscle–fat%) and thoracic supine kyphosis on CT. Methods: Thoracic Cobb angle was measured on supine CT scans from the AtlasDataset by four observers (n=533). Nine muscles were manually labeled on 100 scans (manual cohort). An nnU-Net model was trained on 80 cases with internal validation on 20 cases, then applied to segment the remaining 433 AtlasDataset scans (automated cohort). External segmentation benchmarking was performed on 30 cases from a separate open-source dataset. Associations between supine thoracic curvature and muscle–fat% were evaluated only in AtlasDataset. Results: Manual supine thoracic Cobb angle measurements demonstrated good inter-observer reproducibility (ICC(2,k) = 0.98) with a mean across-rater per-case SD of 3.4°. The nnU-Net achieved Dice scores >0.93 across all nine muscle groups on internal and external segmentation benchmarking. For both manual and automated cohorts, thoracic supine kyphosis correlated with muscle–fat% in the paraspinal (r = 0.35, 0.42), quadratus lumborum (r = 0.29, 0.33), vastus (r = 0.38, 0.32), psoas (r = 0.21, 0.23) and latissimus dorsi (r = 0.21, 0.17) muscles. Conclusions: Automated measurement of trunk muscle–fat% provides a reproducible imaging biomarker correlated with thoracic supine kyphosis on CT. Identifying fatty atrophy of core muscles may help identify potential targets for interventions in hyperkyphotic patients.

Tomography doi: 10.3390/tomography12040058

Authors: Antonio Matteo Amadu Alessio Contena Alberto Dessì Leandra Piscopo Emma Solinas Davide Turilli Salvatore Claudio Fanni Mariano Scaglione Salvatore Masala

Purpose: This study aimed to assess the utility of native T1 mapping for the evaluation of myocardial iron overload in patients with Thalassemia Major. T1 was compared to T2*, which represents the gold standard for iron quantification in the heart and liver. Methods: Consecutive patients with Thalassemia Major who performed cardiac MRI at the University Hospital of Sassari between 2022 and 2024 were prospectively included. All patients underwent a 1.5 T MRI with the same scanner (Philips Ingenia). T2* and native T1 mapping (MOLLI) sequences were performed in all patients on a mid-ventricular single 8 mm short-axis slice of the left ventricle. A region of interest was manually drawn in the septal wall. A T2* value < 20 ms was considered indicative of significant myocardial iron overload. A normal lower limit value of 990 ms was adopted for native T1 mapping. Results: In total, 100 patients with Thalassemia Major were included (median age, 45 [range, 7–80] years; 55% were male). The median myocardial T2* value was 31.4 (range, 5.1–47) and median T1 was 941 ms (range, 557–1131). A total of 12 patients (12%) exhibited T2* values < 20 ms; the T1 values in these patients (median, 733.8 ms [range, 557–975]) were significantly lower compared to those with a T2* of 20 ms or greater (median, 961 ms [range, 820–1131]), p < 0.001. No patient with T2* < 20 ms had a T1 value greater than or equal to 990 ms. Among the 88 patients with T2* ≥ 20 ms, 56 (64%) had T1 < 990 ms (median, 939.2 ms [range, 820–986]). Using a T1 threshold of 990 ms, the sensitivity was 100%, but the specificity was only 36%. ROC analysis identified an optimal T1 value of 895.5 ms, corresponding to 92% sensitivity and 100% specificity. Conclusions: Native T1 mapping is highly sensitive for detecting myocardial iron overload in Thalassemia Major, but the standard 990 ms threshold generates many false-positive results. In our cohort, adopting an ROC-optimized threshold of 895.5 ms markedly improved specificity while preserving excellent sensitivity.

Tomography doi: 10.3390/tomography12040055

Authors: Ryoya Takizawa Shingo Kato Sho Kodama Kazuki Fukui Ryusuke Sekii Naofumi Yasuda Shungo Sawamura Tae Iwasawa Daisuke Utsunomiya

Background: The fat attenuation index (FAI) derived from coronary computed tomography angiography (CTA) is an emerging imaging biomarker of perivascular inflammation. Coronary flow reserve (CFR), assessed by phase-contrast (PC) cine cardiac magnetic resonance (CMR) of the coronary sinus, reflects coronary microvascular function. Although FAI has been linked to adverse outcomes in coronary artery disease (CAD), its relationship with CFR across different CAD stages is not well defined. Methods: We retrospectively evaluated 241 patients (mean age 73.4 ± 10.8 years; 149 men [61.8%]) who underwent both coronary CTA and CMR (122 with known CAD and 119 with suspected CAD). FAI was measured in the proximal left anterior descending (LAD), left circumflex (LCX), and right coronary (RCA) arteries. Impaired CFR was defined as <2.0. Univariable and multivariable logistic regression analyses were performed to identify factors associated with impaired CFR. Results: Impaired CFR was observed in 38 of 122 patients (31.1%) with known CAD and 26 of 119 (21.8%) with suspected CAD. Higher LAD-FAI was associated with impaired CFR in both groups: OR 1.06 (95% CI 1.01–1.11; p = 0.018) in known CAD and OR 1.08 (95% CI 1.02–1.15; p = 0.017) in suspected CAD. Correlation analysis also demonstrated an inverse relationship between LAD-FAI and CFR (p < 0.001), and the strength of association was comparable between the two groups. Conclusions: LAD-FAI was associated with impaired CFR in both suspected and known CAD, with comparable strength of association across the two groups. These findings indicate that perivascular inflammation, reflected by FAI, may relate to coronary microvascular dysfunction in different stages of CAD.

Tomography doi: 10.3390/tomography12040057

Authors: Kristina Ramirez-Garcia Catalina Jaramillo Emma Ferguson Jason Au Erika Odisio Gustavo S. Oderich Daniel Ocazionez Cihan Duran Thanila Macedo

Acute traumatic aortic injury (ATAI) is a rare but life-threatening consequence of blunt trauma that requires prompt diagnosis and accurate imaging assessment. This review presents an imaging-based approach to ATAI, with emphasis on computed tomography angiography (CTA) as the first-line modality for diagnosis, grading, treatment planning, and follow-up. CTA enables the detection of both direct and indirect signs while also allowing for the assessment of lesion severity, extent, and associated findings that may influence management. Familiarity with common mimics and anatomic variants improves diagnostic confidence and helps avoid false positive interpretations. Careful protocol optimization, including multiphasic acquisition, bolus timing, and postprocessing reconstructions, can further enhance image quality and diagnostic performance. Recognition of patient-related and technical CTA artifacts, along with strategies to reduce them, including the selective use of ECG-gated CTA, may further decrease diagnostic uncertainty. We also discuss the complementary roles of emerging CT technologies and magnetic resonance angiography in selected patients. Finally, we review current classification systems, imaging-guided management, post-treatment surveillance, and potential complications. Awareness of ATAI imaging findings, protocol optimization, and diagnostic pitfalls is essential for accurate interpretation and effective multidisciplinary care.

Tomography doi: 10.3390/tomography12040056

Authors: Tae Young Lee Jong Hwa Lee Hoonsub So Ho Min Jang

Background/Objectives: We aim to evaluate whether digital imaging and communications in medicine (DICOM)-level deep learning-based iodine-boosting applied to dual-source dual-energy computed tomography (DECT) source DICOM improves image quality in low-iodine-dose abdominal DECT in adults undergoing post-procedure follow-up computed tomography (CT). Methods: This retrospective study included 43 adults (April–September 2025) who underwent dynamic dual-source DECT using a low-iodine protocol. Three CT reconstructions were compared: mixed images, conventional 50-keV virtual monoenergetic images (VMIs), and 50-keV VMIs generated after applying DICOM-based deep learning iodine-boosting/denoising to the tube-specific dual-energy source DICOM series prior to VMI/iodine-map reconstruction (deep learning-based reconstruction [DLR]-VMI). Iodine material density (IMD) images were compared between the conventional and DLR-processed datasets. Quantitative attenuation and signal-to-noise ratio (SNR) were assessed using paired and repeated-measures tests. Image quality was scored by two readers using a five-point Likert scale. Results: Attenuation varied across CT reconstructions for all regions of interest in both phases (all overall p < 0.001). Liver attenuation increased from 94.9 ± 22.0 Hounsfield units (HU) (VMI) to 114.5 ± 34.6 HU (DLR-VMI) during the arterial phase and from 127.6 ± 25.6 HU to 166.6 ± 39.9 HU during the portal venous phase (both p < 0.001). Liver SNR improved with DLR-VMI compared to VMI (arterial: 9.11 ± 3.62 vs. 6.06 ± 1.90; portal: 12.74 ± 3.56 vs. 7.90 ± 1.82; both p < 0.001). On IMD images, DLR increased HU-equivalent values and liver SNR (arterial: 5.20 ± 2.89 vs. 2.61 ± 1.39; portal: 9.22 ± 2.81 vs. 4.48 ± 1.28; both p < 0.001). Qualitatively, DLR-VMI yielded the highest overall image-quality scores for both reviewers in both phases (Reviewer 1, arterial/portal: 4 (4–5)/5 (4–5); Reviewer 2, arterial/portal: 4 (3–4)/4 (4–4)). DLR also improved the overall image quality of IMD images for both reviewers (all p < 0.001). Conclusions: Raw DICOM-level iodine-boosting DLR applied to dual-source DECT-source DICOM enabled enhanced image quality and improved quantitative and qualitative metrics in low-iodine-dose abdominal DECT.

Tomography doi: 10.3390/tomography12040054

Authors: Aynur Aktas Diptasree Mukherjee Danielle Boselli Brandon N. VanderVeen Lejla Hadzikadic-Gusic Rebecca S. Greiner Michelle L. Wallander Declan Walsh Kunal C. Kadakia

Background/Objectives: Young women with breast cancer (aged ≤ 40 years) have distinct prognostic characteristics, yet little is known about how modifiable body composition factors influence outcomes in this age group. This study examined whether CT-derived body composition measures could identify thresholds that predict overall survival (OS). Methods: This was a single-center, 10-year, matched case–control study nested within a cohort, utilizing retrospectively collected data. Using an institutional database (2009–2018) and the initial cohort of 112 patients, we performed a subset analysis of patients with stage I–III breast cancer at diagnosis who had available pretreatment CT scans to estimate associations with body composition metrics and OS. The final analytic dataset included 89 individuals (49 survivors and 40 deceased). CT scans at the L3 level were analyzed using Slice-O-Matic software to quantify visceral (VAT), subcutaneous (SAT), intermuscular (IMAT), total adipose tissue (TAT), skeletal muscle density (SMD), skeletal muscle gauge (SMG), and skeletal muscle index (SMI). Cox proportional hazard models determined optimal cutpoints for OS. Multivariable models included adjustments for disease stage and hormone receptor status. Results: The median age was 35 (IQR, 32–38); 47% were White and 37% were Black. The majority (78%) were not Hispanic or Latina. Most (67%) were overweight/obese. Specific thresholds for IMAT index (>2.57), VAT (>31.38), and SMG (<2419.89) were associated with worse survival (all p < 0.05), while no cutpoints were identified for other variables. Conclusions: These findings show that muscle fat infiltration and reduced muscle quality have important prognostic value in young women with breast cancer. Exploratory cutpoints derived from routine staging CT scans may help inform risk stratification and generate hypotheses for targeted nutritional or exercise interventions, but require validation in larger, independent cohorts before clinical application.

Tomography doi: 10.3390/tomography12040053

Authors: Ilhaam Alsaati Khaleel Alyahya Mohammed Alharbi Zuhal Y. Hamd Shaden Alhegail

Background: Epidural procedures benefit from a pre-procedural informed estimation of epidural depth, as anticipating the approximate distance can support safer needle placement and reduce technical difficulties during analgesia or anesthesia procedures. The influence of ethnicity has been established across different populations worldwide; however, there is a lack of Saudi-specific MRI data on epidural depth among the adult population. Aim of this Study: To measure the skin to epidural space distance (SED) at the lumbar interspaces L3–L4 and L4–L5 in the Saudi adult population using magnetic resonance imaging (MRI) and to examine its correlations with age, sex, height, weight, and body mass index (BMI). Methods: In this retrospective cross-sectional study, sagittal T1-weighted lumbar MRI images of the spine of 169 adult Saudi patients were studied. The age group ranged from 20 to 70 years, with an equal distribution of males and females. The skin to epidural space distance (SED) was measured at the L3–L4 and L4–L5 interspaces, and its correlations with age, sex, height, weight, and BMI were analyzed. Results: The average measurement of skin to epidural space distance (SED) was 59.08 mm in L3–L4, and 63.21 in L4–L5. BMI and weight showed strong positive correlations with SED across both levels. Female sex was associated with longer SED values at L4–L5. There was no significant correlation between SED and age or height of the patients. Conclusions: MRI-based assessment of SED revealed strong correlations with weight and BMI, but no correlation with height, age, and sex. These findings support the individualized estimation of epidural depth and needle length selection to enhance procedural safety and reduce complications.

Tomography doi: 10.3390/tomography12040052

Authors: Ozden Bedre Duygu Figen Govsa Anil Murat Ozturk Gokhan Gokmen

Background and Objectives: The association between spinal and pelvic alignment significantly impacts sagittal balance in adults. This study, that is retrospective, aims to investigate sagittal alignment anatomy of the pelvis and spine in juvenile idiopathic scoliosis (JIS) and adolescent idiopathic scoliosis (AIS) patients. Materials and Methods: We evaluated nine sagittal parameters from lateral radiographs of 100 JIS and AIS patients, including thoracic kyphosis (TKA), lumbar lordosis (LLA), pelvic tilt (PTA), pelvic incidence (PIA), spinosacral (SSA), sacral slope (SSLA), C7 tilt angles (C7-TA), sagittal vertical axis length (SVAL), and odontoid process hip axis angle (OPHAA) using the ImageJ program. Participants were classified based on their coronal curve group. Analysis of variance compared parameters between curve groups, and Pearson coefficients assessed the relationship between all parameters (p < 0.05). Results: Female participants had an average age of 13.4, and male participants had an average age of 13.0. Female participants had an average scoliosis degree of 19.3, while male participants had 15.2. PIA, PTA, SSLA, and SSA values were significantly higher in women participants than in men participants (p < 0.05). Additionally, PIA, PTA, SSLA, SSA, and OPHAA values were significantly lower in participants with a lower scoliosis degree (p < 0.05). We observed a moderately positive association between LLA and TKA, PIA, SSA, and C7-TA. There was also a moderate positive association between spinopelvic alignment parameters and the degree of scoliosis in participants. Conclusions: Easily measured values such as PIA, PTA, SSLA, SSA, and OPHAA may be related to severity of vertebral column deformities in patients, making them valuable for monitoring scoliosis patients.

Tomography doi: 10.3390/tomography12040051

Authors: Oguzhan Tokur Koray Bingol

Background: This study aims to evaluate the prevalence, spectrum, and coexistence of anatomical variations in the major branches of the abdominal aorta using Multidetector Computed Tomography (MDCT) angiography, with a specific emphasis on analyzing sex-related differences in a large-scale cohort. Methods: A retrospective analysis was conducted on 1174 patients (63.8% male, 36.2% female; mean age 60.54) who underwent abdominal CT angiography between January 2023 and June 2024. Images were acquired using a 128-slice MDCT scanner and reconstructed for detailed vascular assessment. Statistical comparisons between genders were performed using Chi-square and Fisher–Freeman–Halton tests, with p < 0.05 considered significant. Results: The celiac trunk (93.3%), superior mesenteric artery (SMA) (97.1%), and inferior mesenteric artery (IMA) (98.5%) predominantly showed classical patterns. However, significant sex-related differences were identified. Females exhibited significantly higher rates of classical patterns for the celiac trunk (96.2% vs. 91.7%), IMA (99.1% vs. 98.1%), right hepatic artery (RHA) (91.5% vs. 82.6%), and left hepatic artery (LHA) (95.8% vs. 85.4%). Conversely, males showed a higher prevalence of complex variations, including replaced/accessory hepatic arteries and the absence of the common hepatic artery. The number of right and left renal arteries was similar between sexes and did not show a significant difference, while horseshoe kidney was detected only in males. Conclusions: Abdominal vascular structures adhere to classical anatomy more frequently in females, while males exhibit greater morphological variability. These findings emphasize the necessity of gender-specific preoperative vascular mapping to optimize surgical outcomes and reduce morbidity.

Tomography doi: 10.3390/tomography12040050

Authors: Yong-Jin Park Il-Hyun Kim Young-Sil An Joon-Kee Yoon Su Jin Lee

Objectives: The objective of this study is to investigate the clinical potential of generating artificial bone scintigraphy (aBS), defined here as a deep learning-generated bone scintigraphy image that simulates delayed-phase bone scintigraphy (dBS) characteristics, from early-phase bone scintigraphy (eBS) obtained with a short waiting time using an unpaired image-to-image translation method in patients with breast cancer (BC). Methods: In this single-center prospective study involving 245 patients with BC (195 for training and 50 for testing), eBS and dBS were performed. Using the contrastive unpaired translation (CUT) model, we trained with anterior and posterior images of the eBS and dBS from the training group. We then generated aBS images targeting dBS by inputting eBS from the test group for both anterior and posterior views. We conducted quantitative, qualitative, and visual assessments to evaluate aBS. Results: The points of the anterior and posterior images of aBS on the qualitative four-point and five-point rating scales were significantly higher than those of eBS (p < 0.0001). Three nuclear medicine physicians performed visual assessments, demonstrating consistent findings on the presence of bone metastases in both aBS and dBS. Their visual evaluations indicated that the bone-to-soft tissue contrast in aBS was superior to that in eBS. The quantitative metrics of aBS were superior to those of eBS. However, aBS was inferior to the targeted dBS in terms of qualitative and visual assessments. Conclusions: The aBS generated through CUT was superior to eBS in quantitative, qualitative, and visual assessments and preserved lesion-related information comparable to dBS. Although these findings do not support replacement of dBS for definitive diagnosis, they support the feasibility of aBS as an assistive delayed-phase-like image generation approach from earlier-acquired bone scintigraphy.

Tomography doi: 10.3390/tomography12040049

Authors: Anna Frixou Theodoros Leontiou Efstathios Stiliaris Costas N. Papanicolas

Background/Objectives: Modern tomographic reconstruction methods—including physics-informed and AI-based approaches—can produce very high fidelity images. In this regime, widely used global image quality metrics often approach saturation, making it harder to distinguish residual differences between methods and identify remaining performance gaps. This study introduces a physically grounded and standardized evaluation framework designed to retain sensitivity beyond conventional global metrics and support both comparison and systematic improvement in tomographic reconstruction methods. Methods: The proposed framework defines standardized reference images—“Source”, “Detector”, “Ideal”, and “Realistic”—using Monte Carlo simulations, with the Ideal Image serving as a physically grounded benchmark. Reconstruction performance is evaluated using pixel-wise difference and χ2 maps, Region-of-Interest analysis, intensity (gray-value) histogram comparisons, and the Structure and Contrast Index (SCI), computed on difference maps. Demonstrations use simulated SPECT data reconstructed with ART, MLEM, and RISE-1. Results: Across case studies, SCI and χ2-based diagnostics reveal structured residuals and localized deficiencies not evident from global similarity metrics such as SSIM or NMSE. Comparative analyses show that methods with similar global scores can exhibit distinct residual structures and region-specific performance variations, while improved agreement in the sinogram domain does not necessarily translate into improved image fidelity. Histogram-based diagnostics provide complementary information on intensity redistribution not captured by pixel-domain summaries. Conclusions: The framework provides a reproducible, physically meaningful, and sensitive approach for evaluating tomographic reconstruction performance in the high-fidelity regime. By combining standardized reference images with multi-domain and multi-metric analysis, it enables robust benchmarking and supports physically consistent interpretation of reconstruction quality.

Tomography doi: 10.3390/tomography12040048

Authors: Curtis Plante Andrew E. Warfield Carlos Escobedo Amer M. Johri David S. Majdalany Bill S. Majdalany

Background/Objectives: Radiofrequency (RF) wire energy can be used for tissue ablation across many conditions. Adjusting RF generator parameters allows RF energy to puncture tissue with minimal adjacent damage. When RF energy is applied to tissue, however, microbubbles are produced, obstructing intravascular ultrasound (IVUS). Mitigation of RF-generated microbubbles has been studied for ablation but not for puncture. Methods: This paper describes an in vitro bench study using ex vivo bovine live tissue. A model was created with bovine liver tissue and an IVUS catheter submerged in a saline bath. Tissue was punctured with an RF guidewire to recreate microbubbles. Following the puncture, various methods were applied: altering the mechanical index of the IVUS, applying a VF10-5 Linear probe (Siemens), and applying a L12-3 Linear probe (Philips). Regions of interest (ROIs) were selected to track pixel brightness as a proxy for microbubbles. Results: The control increased ROI brightness by 1.5%. Altering the mechanical index of IVUS reduced ROI brightness by 1.2%. VF10-5 probe application increased ROI brightness by 1.2%. L12-3 probe application reduced ROI brightness by 33.0% (p = 0.046, n = 3, one-sample t-test). Brightness reduction was most pronounced at the site of initial RF wire puncture, where microbubbles accumulated. Tip visualization improved, allowing for more precise wire trajectory adjustments. Conclusions: External US with an L12-3 probe was able to dissipate microbubbles effectively to improve IVUS guidance following RF wire puncture in an in vitro exploratory bench model.

Tomography doi: 10.3390/tomography12040047

Authors: Adil Aytaç Bahar Yanık Keyik Erdoğan Bülbül Gülen Demirpolat Gülay Turan

Objective: This study aimed to investigate the associations between intratumoral and peritumoral apparent diffusion coefficient (ADC) measurements and multiparametric biological markers in invasive breast cancer using a novel peritumoral analysis approach. Materials and Methods: In this retrospective study, 68 patients underwent 1.5 T breast magnetic resonance imaging. Following volumetric tumor segmentation, the peritumoral environment was analyzed using a segmentation-based, improved multilayered concentric shell model at distances of 0–2, 2–5, and 5–10 mm. The ADC values were normalized to contralateral parenchyma (rADC), and the intratumoral-to-peritumoral ADC ratios were calculated. Parameters were correlated with molecular subtypes, axillary metastasis, lymphovascular invasion (LVI), histologic grade, and Ki-67 index. Results: Lower intratumoral ADC and lower intratumoral-to-peritumoral ADC ratios were significantly associated with higher histologic grade, increased Ki-67, and axillary metastasis (p < 0.05). The 0–2 mm shell, representing the immediate invasion front, demonstrated the strongest associations with lymphovascular invasion and nodal involvement, while distance-dependent attenuation of effect sizes was observed across more distal peritumoral layers. Conclusions: The segmentation-based and improved multilayered shell model effectively captures the distance-dependent biological gradient of the peritumoral microenvironment. The intratumoral-to-peritumoral ADC ratios within the immediate 2 mm zone may provide complementary information regarding imaging markers of tumor aggressiveness when interpreted alongside absolute measurements. These findings suggest a potential role for these parameters as complementary imaging markers in preoperative risk stratification within a multiparametric framework.

Tomography doi: 10.3390/tomography12040046

Authors: Chao Li Jinwei Zhang Alexey V. Dimov Anne K. Koehne de González Martin R. Prince Jiahao Li Dominick Romano Pascal Spincemaille Thanh D. Nguyen Gary M. Brittenham Yi Wang

Background/Objectives: Liver fibrosis, if left untreated, can lead to cirrhosis and cancer. The current standard liver biopsy for fibrosis staging is invasive and prone to risks of complication. The objective of this study was to develop a new noninvasive method to quantify fibrosis using diamagnetic susceptibility sources generated from multi-echo gradient echo (mGRE) data with both magnitude decay R2* modeling and phase QSM modeling. Methods: mGRE data of ex vivo liver explants was processed with fat–water separation and then susceptibility source separation. Negative susceptibility was used to measure diamagnetic fibrosis. In 20 formalin-fixed liver explant sections, negative susceptibility maps were compared with other MRI parameters against pathology for fibrosis staging. Results: The correlation between the negative susceptibility sources and the fibrosis stages was evaluated with Spearman coefficients. Negative susceptibility differentiated (i) no or mild fibrosis (stages F0 to F1) from moderate-to-advanced fibrosis (stages F2 to F3; p = 0.0025), (ii) stages F2 to F3 from cirrhosis (stage F4; p = 0.021), and (iii) no-to-moderate fibrosis (stages F0 to F2) from advanced fibrosis or cirrhosis (stages F3 to F4) with a sensitivity of 90%, a specificity of 90%, and a 0.88 Receiver Operating Characteristic Area Under the Curve (AUC) (p = 0.0017). Conclusions: For staging fibrosis, negative susceptibility was superior to other MRI parameters, including R2*, QSM, and PDFF. Negative susceptibility sources were positively correlated with the fibrosis stage (r = 0.60). Negative susceptibility could be valuable for MRI staging in liver fibrosis.

Tomography doi: 10.3390/tomography12040045

Authors: Mohammad Alarifi Areej Aloufi Abdulrahman Jabour Ahmad Abanomy Haitham Alahmad Khaled Alenazi Alhanouf Alshedi Mansour Almanaa

Background/Objectives: Breast cancer screening such as mammography supports earlier detection, but variability in interpretation can still lead to missed cancers and avoidable follow-up testing. We evaluated ChatGPT-5.2 Thinking (OpenAI) as a stand-alone model for examination-level malignancy classification on standard bilateral mammography views in a biopsy-referred cohort, compared with breast radiologists, and assessed laterality performance. Methods: We conducted a retrospective, multicenter diagnostic-accuracy study across breast imaging centers in Saudi Arabia. From an upstream screened cohort (n = 1225), we constructed a biopsy-referred test set of 100 mammography examinations (four 2D views per exam: bilateral CC and MLO; 400 images), including 61 biopsy-confirmed malignancies and 39 biopsy-negative controls, with pathology as the reference standard. Radiologists were blinded to pathology and AI outputs and assigned BI-RADS (0–5) and suspected laterality. ChatGPT-5.2 interpreted the same de-identified views using a BI-RADS-guided prompt to generate BI-RADS and laterality. The sensitivity, specificity, accuracy, and laterality concordance were then estimated. Results: ChatGPT-5.2 had higher sensitivity than radiologists (95.08% vs. 81.97%) but markedly lower specificity (10.26% vs. 56.41%), resulting in lower overall accuracy (62.00% vs. 72.00%). The AI produced 58 true positives, 35 false positives, and 3 false negatives, while radiologists produced 50 true positives, 17 false positives, and 11 false negatives. Laterality accuracy among malignant examinations was 60.66%. Conclusions: In this pathology-anchored, biopsy-referred evaluation, ChatGPT-5.2 identified more cancers but generated substantially more false-positive classifications and showed only moderate breast-side localization. These findings support use as a concurrent aid or prioritization tool rather than a stand-alone reader and motivate efforts to improve specificity and laterality before prospective validation.

Tomography doi: 10.3390/tomography12040044

Authors: Muhammad Adeel Asghar Sultan Shoaib Muhammad Zahid

Background: The Magnetic Resonance Imaging (MRI)-based tumor segmentation remains a challenging problem in medical imaging due to tumor heterogeneity, unpredictable morphological features, and the high complexity of calculations needed to implement it in clinical practice, putting it out of the scope of real-time applications. Although neural networks have significantly improved segmentation performance, they still struggle to capture morphological tumor features while maintaining computational efficiency. This work introduces Hybrid Adaptive Attention U-Net (HAAU-Net) framework, combining context-aware morphologically stable features and spatial channel attention to achieve high-quality tumor segmentation with less computational cost. Methods: The proposed HAAU-Net framework integrates multi-scale Adaptive Attention Blocks (AAB), Context-Aware Morphological Feature Module (CAMFM) and Spatial-Channel Hybrid Attention Mechanism (SCHAM). CAMFM is used to maintain the stability of morphological features by hierarchical aggregation and dynamic normalization of features. SCHAM enhances feature representation by modelling channels and spatial regions where the strongest feature are determined to use in segmentation. On the BRaTS 2022/2023 data, the proposed HAAU-Net is evaluated using four modalities including T1, T1GD, T2 and T2-FLAIR sequences. Results: The proposed model able to obtain 96.8% segmentation accuracy with a Dice coefficient of 0.89 on the entire tumor region, outperforming the alternative U-Net (0.83) and conventional CNN methods of segmentation (0.81). The proposed HAAU-Net architecture cuts the computational complexity of the standard deep learning models by 43% and still achieve real-time inference (28 FPS on a regular GPU). The hybrid model used to predict survival has a C-Index of 0.91 which is higher than the traditional SVM-based methods (0.72). Conclusions: Spatial-channel attention, combined with morphologically stable features, can be combined to allow clinically significant interpretability in attention maps. The proposed framework significantly improves segmentation performance while maintaining computational effeciency. This broad system has a serious potential of AI-enabled clinical decision support system and early prognostic diagnosis in neuro-oncology with practical deployment capability.

Tomography doi: 10.3390/tomography12030043

Authors: Bahadir Caglar Suha Serin

Background/Objectives: Radiological assessment has become indispensable for modern clinical decision-making. Image quality plays a critical role in the reliability of radiological interpretation. Unlike most previous studies, this study investigated the effect of monitor type on diagnostic accuracy and ease of diagnosis under physical conditions outside the radiology unit. Methods: Three image sets were prepared for the study, consisting of emergency radiological images, each containing 50 computed tomography, magnetic resonance imaging, and digital radiography images. The image sets were examined by five emergency specialists, who were blinded to each other’s work, under emergency service conditions on a standard monitor (SM), medical monitor (MM), and advanced monitor (AM). The accuracy and ease of diagnosis were analyzed statistically according to the type of monitor used. Results: Overall diagnostic accuracy rates were 98.7% for SM, 100% for AM, and 100% for MM. Cochran’s Q test demonstrated a statistically significant difference between monitor types (p = 0.002), with significant pairwise differences for SM–AM and SM–MM comparisons. The absolute risk difference between SM and AM/MM was 1.3%, corresponding to a relative risk of 1.013 and a number needed to benefit (NNB) of 77. Ease of diagnosis scores increased progressively across monitor types (SM: 7.6 [IQR 7–8], AM: 9.4 [IQR 9–9.8], MM: 9.8 [IQR 9.6–10]; p < 0.001), with a large overall effect size (Kendall’s W = 0.81). Multilevel modeling confirmed that these associations persisted after adjustment for clustering effects. Conclusions: In situations where medical monitors cannot be used due to cost and operational constraints, opting for advanced monitors instead of standard monitors may modestly improve diagnostic accuracy while substantially enhancing perceived ease of diagnosis.

Tomography doi: 10.3390/tomography12030042

Authors: Seung Hoon Lee Phil Kyu Lee Se Eun Park Ho Ra Jiwon Baek

Background/Objectives: To comprehensively evaluate longitudinal retinal and choroidal vascular changes after phacoemulsification using automated optical coherence tomography angiography (OCTA) analysis and to investigate clinical factors influencing these changes. Methods: This retrospective study included 26 subjects (31 eyes) who underwent uncomplicated phacoemulsification. OCTA was performed at baseline and at 1 day, 1 week, 1 month, and 2 months postoperatively. Automated quantitative analysis was applied to assess vessel density- and structure-related parameters in the superficial capillary plexus (SCP), deep capillary plexus (DCP), choriocapillaris, and Haller layer. Longitudinal changes were analyzed using repeated-measures analysis of variance, with time × clinical factor interactions evaluated for diabetes mellitus, anesthesia method, and sex. Inter-layer associations were assessed using Spearman correlation analysis. Results: Significant longitudinal changes were observed in retinal layers. In the SCP, vessel density increased from 42.59 ± 1.46 at baseline to 44.10 ± 1.44 at 2 months (p = 0.002), accompanied by increases in vessel length and node counts (all p < 0.001). In the DCP, vessel density increased from 34.66 ± 5.98 to 38.65 ± 4.83 (p < 0.001). In contrast, choriocapillaris-related parameters showed no significant overall time effect. In the Haller layer, mean vessel diameter decreased significantly over time (p < 0.001), while density-related metrics remained unchanged. ΔVAD demonstrated positive correlations between adjacent layers (SCP–DCP and DCP–choriocapillaris) and a negative correlation between choriocapillaris and Haller layers. Diabetes mellitus showed no significant longitudinal effect, whereas retrobulbar anesthesia and sex significantly modified selected choroidal trajectories. Conclusions: Automated and integrated OCTA analysis revealed layer-dependent retinal and choroidal vascular responses after phacoemulsification, with coordinated changes confined mainly to anatomically adjacent layers and selective modulation by clinical factors.

Tomography doi: 10.3390/tomography12030041

Authors: Konstantin P. Gaikovich Ilya V. Malyshev Dmitry G. Reunov Nikolay I. Chkhalo

Background/Objectives: This article develops theory and methods for 3D tomographic imaging of absorption coefficient distributions using axial scanning with EUV microscopes at 46× and 345× magnification. Unlike conventional CT that requires sample rotation, axial scanning moves cells through the microscope focus. The aim is tomographic reconstruction of living cell fine structure without the organelle staining used in optical fluorescence microscopy or ultra-thin cell slicing as in electron microscopy. Methods: By generalizing the geometric-optical approximation for small absorption coefficient inhomogeneities in absorbing media, we derived a new explicit tomography equation and solution algorithm validated through numerical simulation. The approach was applied to Convallaria cell analysis using the ×46 microscope. For the ×345 microscope, we developed an alternative method where the kernel of the tomography integral equation was determined experimentally using gold nanospheres with known absorption coefficient, shape, and position. This method was tested through modeling and applied to diagnostics of Convallaria and mouse cerebellar granule cells. Results: The developed methods resolve subcellular features down to 140 nm using the ×46 microscope and 50 nm using the ×345 microscope. Thin low-contrast intracellular structures and individual 50–100 nm organelles were detected. Conclusions: Methods for retrieving absorption coefficient distributions in cone-beam geometry based on geometric-optical theory generalization and on calibration by gold nanoparticles have been developed and validated through numerical simulation and cell analysis. These methods demonstrate for the first time the effectiveness of axial nanotomography using multilayer mirror microscopes for cell diagnostics.

Tomography doi: 10.3390/tomography12030040

Authors: Akira Ogihara Takeshi Fukuda Shunsuke Katsumi Hiroya Ojiri

Background: Although magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for most spinal evaluations, computed tomography (CT) is still always required for preoperative planning to assess osseous anatomy and determine surgical device size, increasing the radiation exposure and workflow complexity. CT-like images enable visualization of precise bone morphology without ionizing radiation. In addition, these images often provide CT myelography-like contrasts, allowing the simultaneous depiction of the spinal canal area (SCA). This study aimed to evaluate whether CT-like images provide measurement accuracy equivalent to conventional CT and MRI for pedicle screw planning and spinal canal area assessment. Methods: We retrospectively analyzed paired lumbar CT and MRI datasets obtained within ≤1 month in 51 patients. Pedicle width and length were measured on CT and CT-like images, whereas SCA was measured on T2 weighed-images and CT-like images. A total of 224 vertebrae were analyzed. Annotated images were independently evaluated by two readers in a randomized order. Inter-modality agreement was assessed using intraclass correlation coefficients (ICCs) and a Bland–Altman analysis. Results: CT-like images demonstrated an excellent agreement with CT for pedicle measurements (ICCs: 0.968–0.985 for width; 0.922–0.966 for length). Mean differences were ≤0.1 mm for pedicle width and approximately 1 mm for pedicle length, which are unlikely to affect screw selection. The agreement with T2WI for SCA was good to excellent (ICCs: 0.766–0.945). Conclusions: CT-like images provide comparable performance for quantitative pedicle assessment and show high agreement for SCA evaluation, supporting comprehensive preoperative assessment with a single MRI examination.

Tomography doi: 10.3390/tomography12030039

Authors: Davut Unsal Capkan Ibrahim Tayfun Sahiner

Background/Objectives: Post-cholecystectomy syndrome (PCS) remains diagnostically challenging due to overlapping biliary and non-biliary causes. This study aimed to evaluate whether common bile duct (CBD) diameter measured by MRCP can serve as a practical triage parameter in symptomatic PCS patients and to define a data-supported threshold for predicting clinically relevant biliary pathology. Secondary objectives included assessing correlations between MRCP findings and upper endoscopic features. Methods: In this retrospective single-center study, symptomatic adults undergoing upper endoscopy and MRCP were analyzed. Demographic, clinical, biochemical, radiologic, and endoscopic variables were recorded. Diagnostic performance was assessed using ROC analysis, and independent predictors of biliary dilatation were evaluated with multivariable logistic regression. Results: We analyzed 141 symptomatic post-cholecystectomy patients (mean age 58.2 ± 16.3 years; 67.4% female; median time since surgery 18 [9–36] months). Major symptoms: abdominal pain 84.9%, dyspepsia/bloating 47.5%, nausea/vomiting 22.3%, diarrhea 15.1%. CBD diameter measurements were available in the MRCP subgroup (n = 45); ERCP was performed selectively (n = 12). MRCP findings: CBD ≥ 7 mm 31.9%, biliary dilatation 14.9%, stricture 2.8%, suspected Oddi dysfunction 11.3%, postoperative complications 39.7%. Endoscopy: mucosal inflammation 91.5%; normal 8.5%. Significant correlations included CBD diameter vs. mucosal inflammation (r = 0.32, p = 0.001), dilatation vs. bile reflux (r = 0.28, p = 0.004), and Oddi dysfunction vs. papillary edema (r = 0.41, p = 0.001). CBD diameter showed the best diagnostic performance (AUC 0.82, 95% CI 0.74–0.90; cut-off ≥ 8.0 mm; sensitivity 78.3%; specificity 81.5%; p < 0.001). In multivariable analysis, age independently predicted biliary dilatation (OR 1.05 per year; 95% CI 1.01–1.09; p = 0.007). Conclusions: In symptomatic post-cholecystectomy patients, MRCP-measured CBD diameter provides a useful metric for risk stratification, with a threshold of ≥8 mm identifying patients more likely to harbor biliary pathology. These findings support a structured diagnostic approach that prioritizes noninvasive imaging while reserving ERCP for selected cases. Further prospective validation is warranted.

Tomography doi: 10.3390/tomography12030038

Authors: Md Zobaer Islam Timothy G. Perk Amy Weisman Mark C. Markowski Kenneth J. Pienta Young E. Whang Matthew I. Milowsky Martin G. Pomper Nicholas Wisniewski Ralph A. Bundschuh Rudolf A. Werner Michael A. Gorin Steven P. Rowe

Objectives: This study evaluated the test–retest repeatability of semi-quantitative and volumetric features derived from artificial intelligence (AI)-assisted lesion segmentation on 18F-DCFPyL Prostate Specific Membrane Antigen (PSMA)-PET/CT imaging of patients with prostate cancer (PCa). Specifically, we assessed the reliability of maximum, minimum and total standardized uptake values (SUVmax, SUVmean, SUVtotal) and lesion volume measurements across varying lesion sizes and explored the implications of variability for clinical decision-making. Methods: We analyzed 18F-DCFPyL PSMA-PET/CT images from 22 patients with metastatic PCa. Lesion segmentation was performed using the AI-guided TRAQinform IQ technology, followed by a manual review to eliminate potential false-positive sites of uptake. Lesion-level test–retest repeatability was evaluated using 95% limits of agreement (LOA), intra-class correlation coefficient (ICC), within-subject coefficient of variation (wCOV) and Bland–Altman analysis for SUV and volumetric parameters. Lesions were stratified by size (>1 cm3 and >1.5 cm3) to assess the impact of lesion volume cut-offs on measurement variability. Results: A total of 297 lesions were analyzed, including 191 lesions > 1 cm3 and 161 lesions > 1.5 cm3. Test–retest variability was higher in smaller lesions, with narrower LOA and lower wCOV for larger lesions. SUVmax and SUVmean exhibited lower variability than SUVtotal and lesion volume. The 95% LOA for SUVmax ranged from −33.81% to +38.02% for all lesions, improving to −31.82% to +31.01% for lesions > 1.5 cm3. Similar trends were observed for SUVmean, SUVtotal, and volume. Bland–Altman plots confirmed reduced variability in larger lesions, with no significant systematic bias. Conclusions: The test–retest repeatability of AI-assisted PSMA-PET/CT features varies by feature type, with semi-quantitative features demonstrating improved repeatability relative to volumetric features. Additionally, repeatability is influenced by lesion size, with larger lesions exhibiting greater reliability. These findings highlight the importance of lesion size-dependent thresholds in response assessment and variability-aware feature selection in prognostic models. Current algorithms may be better optimized for larger lesions and higher volumes of disease, with limitations remaining in the robust detection and segmentation of smaller/more subtle lesions.

Tomography doi: 10.3390/tomography12030037

Authors: Göksel Tuzcu Burak Çildağ Songül Çildağ Çiğdem Yenisey Zahir Kızılay

Background/Objectives: This study aimed to compare in vivo cerebral gadolinium (Gd3+) accumulation, associated unfolded protein response (UPR), and oxidative stress parameters in rats after exposure to gadolinium-based contrast agents (GBCAs). Methods: This study was designed as a controlled, experimental animal study to evaluate the accumulation of Gd3+ in the basal ganglia of rats following the administration of 0.6 mmol/kg gadopentetate dimeglumine (linear) and gadoterate meglumine (macrocyclic). Male Sprague–Dawley rats were exposed to the contrast agents for 24 and 72 h, and then the basal ganglia tissues were collected postmortem. The tissue levels of Gd3+ accumulation, activating transcription factor-6 (ATF6), inositol-requiring enzyme-1 (IRE-1), protein kinase RNA-like endoplasmic reticulum kinase (PERK), damage-inducible transcript-3 (DDIT3), total antioxidant status (TAS), and total oxidant status (TOS) were determined. Results: Linear GBCA-treated rats had persistent Gd3+ levels over time, whereas a significant reduction from 24 to 72 h was observed in macrocyclic GBCA-treated rats (p < 0.001). PERK, DDIT3, and ATF6 expressions were significantly elevated after linear GBCA exposure (p < 0.05), while no significant increase was observed in the macrocyclic GBCA-treated group. However, IRE-1, TAS, and TOS levels were not significantly different in either group. Conclusions: Linear and macrocyclic GBCAs demonstrated distinct patterns of cerebral Gd3+ accumulation and UPR levels in rats. Accordingly, GBCA administration should be reserved for instances where it is necessary, such as when contrast enhancement is clinically required.

Tomography doi: 10.3390/tomography12030036

Authors: Chiara Zanon Alessandro Fiocco Vincenzo Tarzia Emilio Quaia

Background: Transcatheter aortic valve implantation (TAVI) is increasingly performed in fluoroscopy-intensive environments, raising concerns about occupational eye lens dose (equivalent dose to the eye lens, Hp (3)) and the risk of radiation-induced cataract, particularly after the reduction of recommended annual eye lens dose limits to 20 mSv. Purpose: To summarize evidence on eye lens radiation exposure during TAVI, identify procedural and occupational determinants, and review strategies to reduce exposure with a focus on imaging optimization. Methods: We performed a narrative review of observational and prospective studies reporting direct eye-level dose measurements or validated surrogate eye lens dose estimates (head-level, chest-level, or DAP-normalized) during TAVI and related structural heart procedures. This approach was chosen to provide a qualitative synthesis of the available evidence rather than a formal systematic review. Results: Reported operator eye lens doses typically ranged from 30 to 110 µSv per procedure, with higher exposure during transapical/transaortal access and among staff working close to the patient (e.g., anesthesiologists and circulating nurses). Additional shielding and lead-free drapes reduced normalized eye dose by approximately 25–40%, and RADPAD® use reduced operator eye-level dose from 24.3 to 14.8 µSv per procedure (p = 0.008). At these levels, cumulative exposure may approach recommended regulatory limits after approximately 150–300 procedures, depending on role, access route, and shielding practices. Conclusion: In conclusion, Occupational eye lens exposure during TAVI is clinically relevant and strongly influenced by access route, staff positioning, and imaging-system use. Dose reduction should combine routine eye protection and dedicated eye-level dosimetry with imaging optimization (low pulse-rate fluoroscopy, minimized Digital-Subtraction-Angiography (DSA)/cine acquisitions, tight collimation, avoidance of unnecessary magnification, and correct positioning of ceiling-suspended shields and table skirts).

Tomography doi: 10.3390/tomography12030035

Authors: K. Shahgeldi M. Parenmark L. Claesson T. M. Svahn

Purpose: The present study aimed to assess the radiation dose associated with low-dose (LD) CT pelvimetry compared with conventional radiography and to evaluate the adequacy of the resulting image quality. Methods: The absorbed dose was measured using thermoluminescent dosimeters positioned in an anthropomorphic female phantom, including uterine locations, to estimate the fetal dose. Conventional radiographic pelvimetry and LD-CT pelvimetry were performed using clinically implemented protocols. Effective dose was calculated using Monte Carlo–based modeling applying acquisition parameters and retrospective patient dose registry data. Image quality of LD-CT pelvimetry was independently evaluated in 14 consecutive clinical cases using a four-point ordinal scale. Results: LD-CT pelvimetry reduced the mean absorbed pelvic dose by approximately 50% compared with conventional pelvimetry (0.18 vs. 0.39 mGy) and decreased estimated fetal dose by 40% (0.21 vs. 0.37 mGy). These estimates were based on standardized single acquisitions and did not incorporate additional radiation from retakes commonly observed in conventional practice. CT demonstrated substantially more homogeneous dose distribution, whereas conventional pelvimetry exhibited marked heterogeneity with peak values up to 2.3 mGy. The maternal effective dose was lower for LD-CT (0.16 mSv) than for conventional pelvimetry (0.36 mSv); inclusion of retakes increased the conventional effective dose to 0.71 mSv. All CT examinations were diagnostically adequate, and no recalls were required. Conclusions: Optimized low-dose CT pelvimetry significantly reduces radiation dose compared with conventional radiographic pelvimetry while maintaining reliable diagnostic image quality. These results support the clinical adoption of CT-based pelvimetry as a dose-efficient and reproducible alternative to conventional techniques.

Tomography doi: 10.3390/tomography12030033

Authors: You Fu Jiasen Feng Hanlin Cheng

Background/Objectives: Automatic segmentation and identification of vertebrae in spinal CT are essential for assisting diagnosis of spinal disorders and for preoperative planning. The task is challenging due to the high structural similarity between adjacent vertebrae and the morphological variability of vertebrae. Most existing methods rely on fully supervised deep learning and, constrained by limited annotations, struggle to remain robust in complex scenarios. Methods: We propose a semi-supervised approach built on a dual-branch 3D U-Net. Mamba modules are inserted between the encoder and decoder to model long-range dependencies along the cranio–caudal axis. The identification branch employs a 3D convolutional block attention module (3D-CBAM) to enhance class discriminability. A unified semi-supervised objective is formulated via teacher–student consistency: for each unlabeled sample, weakly and strongly augmented views are generated, and cross-branch consistency is enforced, together with confidence-based filtering and class-frequency reweighting. In addition, a connected-component analysis is used to enforce anatomically plausible sequential continuity of vertebral indices in the outputs. Results: Experiments on VerSe 2019 and 2020 show that, on the public VerSe 2019 test set (with VerSe 2020 scans used as unlabeled training data), the supervised baseline achieved a Dice score of 89.8% and an identification accuracy of 92.3%. Incorporating unlabeled data improved performance to 91.6% Dice and 97.5% identification accuracy (relative gains of +1.8 and +5.2 percentage points). Compared with competing methods, the proposed semi-supervised model attains higher or comparable segmentation accuracy and the highest identification accuracy. Conclusions: Without additional annotation cost, the proposed method markedly improves the overall performance of vertebra segmentation and identification, offering more robust automated support for clinical workflows.

Tomography doi: 10.3390/tomography12030034

Authors: Estefanía Ruíz Muñoz Leopoldo Altamirano Robles Raquel Díaz Hernández Kelsey Alejandra Ramírez Gutiérrez Saúl Zapotecas-Martínez José de Jesús Velázquez Arreola

Background: The Detection and localization of breast lesions remain challenging in mammography and digital breast tomosynthesis (DBT) due to tissue overlap and information loss during volumetric reconstruction. Sinograms preserve the full angular projection data acquired during scanning, enabling analysis of tissue structure without reconstruction. Methods: This study proposes a direct segmentation approach for mammography and DBT sinograms using a U-Net architecture. Experiments were conducted on 1082 annotated mammography mass images from the CBIS-DDSM dataset (521 benign, 561 malignant) and 272 annotated DBT images from the Breast Cancer Screening DBT dataset (136 benign, 136 malignant). Dataset splitting was performed at the patient level to prevent data leakage, and all reported quantitative results correspond to the independent test set, with the validation set used solely for model selection and early stopping. Three input configurations were evaluated: mammography sinograms, DBT sinograms, and a combined model. Results: The mammography model achieved the highest performance (Dice: 0.94 training, 0.90 test), outperforming DBT alone (0.77 training, 0.70 test). Multimodal fusion improved DBT results (Dice: 0.84 test). Centroid analysis showed 99.11% correspondence with reference annotations (average distance: 2.83 pixels), and partial back-projection reconstructions confirmed anatomical consistency. Compared with YOLOv5x, the proposed approach provided superior lesion localization, particularly for small or multiple lesions. Conclusions: Direct sinogram segmentation is an efficient, clinically viable strategy for breast lesion detection and localization.

Tomography doi: 10.3390/tomography12030032

Authors: Mark Laidlaw Maya Reid Sukanya Rajiv Jean-Marc Gerard

Background/Objectives: Skull base osteomyelitis and necrotising otitis externa require prolonged antibiotic therapy, yet determining optimal treatment cessation timing remains challenging. Conventional imaging modalities demonstrate persistent abnormalities beyond infection resolution, confounding treatment decisions. This systematic review evaluated the diagnostic test accuracy of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) for treatment response monitoring in skull base osteomyelitis and necrotising otitis externa. Methods: We conducted a systematic review following PRISMA-DTA guidelines, searching MEDLINE, Embase, CENTRAL, CINAHL, Scopus, and Web of Science from inception to November 2025. Studies evaluating 18F-FDG PET diagnostic accuracy for treatment response assessment in confirmed skull base osteomyelitis or necrotising otitis externa were included. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2. Bivariate random-effects meta-analysis was performed using MetaBayesDTA to obtain pooled sensitivity and specificity. Results: Eight studies comprising 154 lesions contributed to the primary analysis. Pooled sensitivity was 95.2% (95% credible interval 85.6–99.0%) and pooled specificity was 89.1% (95% credible interval 70.7–96.7%). The positive likelihood ratio was 8.7 (95% credible interval 3.2–28.4) and negative likelihood ratio was 0.05 (95% credible interval 0.01–0.17), with a diagnostic odds ratio of 172.0. Seven studies evaluating detection rate at initial presentation yielded a pooled rate of 96.1% (95% confidence interval 91.3–98.3%). SUVmax was the most frequently used metabolic parameter. Conclusions: 18F-FDG PET, specifically using SUVmax, demonstrates high sensitivity and good specificity for treatment response monitoring, with excellent capacity to rule out persistent infection. However, evidence quality is limited by retrospective designs and substantial heterogeneity. Prospective studies with standardised thresholds are needed to validate clinical utility.

Tomography doi: 10.3390/tomography12030031

Authors: Emilio Quaia

This editorial provides insights into the common situation of paper rejection, which must be managed by the authors [...]

Tomography doi: 10.3390/tomography12030030

Authors: Daniel Uher Gerhard S. Drenthen Tineke van de Weijer Jochem van der Pol Christianne M. Hoeberigs Paul A. M. Hofman Sam Springer Rob P. W. Rouhl Albert J. Colon Olaf E. M. G. Schijns Walter H. Backes Jacobus F. A. Jansen

Background: In temporal lobe epilepsy (TLE), locally reduced glucose metabolism (i.e., hypometabolism) is indicative of the epileptogenic onset zone (EZ). Here, we investigate the potential value of resting-state fMRI (rs-fMRI) for localizing the EZ with fluorodeoxyglucose positron emission tomography (FDG-PET) as ground truth. Methods: Twelve PET-positive patients (34.1 ± 13.1 y; 5 females) with unilateral drug-resistant TLE were included. FDG-PET and rs-fMRI were acquired simultaneously at a hybrid 3T PET-MR scanner. Hypometabolic regions were identified on the FDG-PET images by a nuclear medicine expert. The FDG-PET images were compared with a clinical FDG-PET control dataset with normal glucose uptake distribution. The output z-score maps were thresholded at z < −2 to produce a binary mask of the significantly hypometabolic regions. The hypometabolism masks were mirrored onto the contralateral hemisphere for the asymmetry comparison. Regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) were calculated from the rs-fMRI in conventional (0.01–0.1 Hz) and slow-3 (0.073–0.198 Hz) frequency bands. Asymmetry indices (AIs) were calculated using the ipsilateral and contralateral hypometabolic masks in the PET-positive subjects and assessed via the one-sample Wilcoxon test and Spearman correlation coefficients. Results: The AIs of conventional fALFF were significantly lower in the hypometabolic zone (p < 0.05). A significant negative correlation was found between the AIs of FDG-PET and fALFF in the slow-3 band (r = −0.62; p < 0.05). Conclusions: Conventional and slow-3 band fALFF showed a potential to mimic the FDG-PET findings in terms of EZ localization. Further research with extended cohorts and histopathological validation is required to determine the clinical value.

Tomography doi: 10.3390/tomography12030029

Authors: Daniyal Iqbal Domenec Puig Muhammad Mursil Hatem A. Rashwan

Background/Objectives: Stroke is a leading cause of long-term disability, and predicting functional outcome at discharge, such as the modified Rankin Scale (mRS), is important for guiding treatment and rehabilitation. Many existing approaches depend on advanced imaging or complex corticospinal tract (CST) segmentation from multi-shell diffusion MRI, limiting clinical feasibility. Automated lesion segmentation is also challenging due to lesion heterogeneity and MRI variability. This study proposes a clinically feasible multimodal MRI pipeline based on routine imaging. Methods: Lesion segmentation models were trained and evaluated on the ISLES 2022 dataset (250 training, 150 test cases). Zero-shot external evaluation was performed on 149 cases from ISLES 2024 using standard MRI sequences only. An ensemble of deep learning models (SEALS, NVAUTO, FACTORIZER) was evaluated on ISLES 2022, while SEALS alone was used for external testing. CST segmentation was performed using TractSeg on single-shell diffusion-weighted imaging. Imaging biomarkers included lesion volume, shape, ADC-based texture features, CST integrity, and lesion–CST overlap. These features were used to train machine learning models for binary mRS prediction at discharge. Results: The ensemble achieved a Dice score of 0.82 on ISLES 2022, while zero-shot evaluation on ISLES 2024 achieved 0.57. In exploratory analysis, CatBoost achieved the highest point estimates (accuracy 0.88, F1-score 0.87, ROC-AUC 0.83). Key predictors included lesion–CST overlap, lesion volume, surface area, dissimilarity, and contrast. Conclusions: This exploratory study demonstrates the feasibility of combining automated lesion segmentation with anatomically informed biomarkers using routine clinical MRI, supporting interpretable stroke outcome modelling and motivating future large-scale validation.

Tomography doi: 10.3390/tomography12020028

Authors: Irem Yesiloglu Melissa Stockbridge Zafer Keser

Background: Stroke is a leading cause of mortality and long-term disability, and aphasia is among its most common and debilitating sequelae. Diffusion tensor imaging (DTI) and advanced diffusion imaging techniques enable the assessment of white matter integrity and provide clinically relevant measures in post-stroke aphasia. Methods: We conducted a comprehensive review of studies applying DTI or advanced diffusion imaging to investigate structural connectivity in adults with post-stroke aphasia (PSA). PubMed, CENTRAL, Ovid MEDLINE, and Embase were searched, and eligible studies were synthesized according to their diagnostic, prognostic, or therapeutic focus. Results: Ninety-five studies were included. Of these, 59 were classified as diagnostic, 17 as prognostic, and 19 as therapeutic. Most studies employed conventional DTI (n = 77), while a growing body of research utilized advanced diffusion models, including CSD, DSI, and DKI (n = 18). Conclusions: This comprehensive synthesis demonstrates the evolution of diffusion imaging in PSA research. While conventional DTI has provided foundational insights, advanced diffusion methods offer superior characterization of complex fiber architecture and improved clinical–anatomical correlation. Diffusion-derived markers of dorsal and ventral language pathways were consistently associated with language performance, while connectome-level analyses highlighted the importance of preserved global network architecture for recovery. Continued efforts are needed to translate diffusion imaging findings into clinical applicable biomarkers to guide personalized aphasia rehabilitation, with greater use of advanced methods.

Tomography doi: 10.3390/tomography12020027

Authors: Ting Wang Jing Gong Simin Wang Shiyun Sun Jiayin Zhou Luyi Lin Dandan Zhang Chao You Yajia Gu

Background/Objectives: To investigate and develop a non-invasive parametric radiomics model derived from dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) kinetics for predicting breast cancer molecular subtypes (HR+/HER2−, HER2+ and triple-negative breast cancer). Methods: This multicenter retrospective study enrolled 935 female patients with histologically confirmed breast cancer who underwent pretreatment breast DCE-MRI from August 2017 to July 2022. Based on the wash-in rate (WIR) and the area under the TIC, the original multiphase DCE-MRI images were converted into two types of parametric images. Radiomics features were extracted from TIC-WIR and TIC-Area images and analyzed using low variance filtering, the elimination of highly correlated features, and the least absolute shrinkage and selection operator regression. The categorical boosting algorithm was employed to develop multiclass prediction models for breast cancer molecular subtyping. A TIC-Combined model was further established by integrating the calibrated probability outputs of the TIC-WIR and TIC-Area models using a decision-level fusion strategy. The discrimination, calibration, and interpretability of the models were evaluated in the study datasets. Results: The TIC-Combined model achieved superior predictive performance in both the internal validation set (micro-average AUC: 0.79, macro-average AUC: 0.77) and the external validation set (micro-average AUC: 0.77, macro-average AUC: 0.75). For subtype-specific classification by the TIC-Combined model, the highest one-vs-rest AUCs were 0.81 for triple-negative breast cancer in the internal validation set and 0.76 for HER2+ breast cancer in the external validation set. The TIC-Combined model also showed good calibration and high interpretability which ensured reliable predictions and provided clear insights into feature importance. Conclusions: Interpretable parametric radiomics from TIC-derived parametric maps links kinetic features to molecular phenotypes, enabling accurate and non-invasive classification of breast cancer molecular subtypes.

Tomography doi: 10.3390/tomography12020026

Authors: Asma F. Al-Muhanna Musaed A. Al-Fayez Abdulrahman H. Al-Abdulwahhab Abdulaziz M. Al-Sharydah Mohammed Al-Watban Abdulrazaq Al-Ojan

Background/Objectives: To evaluate and characterize anatomical variations in the sphenoid sinus in the Saudi population using computed tomography (CT). Methods: This retrospective cross-sectional study included patients aged ≥18 years who underwent multi-detector CT (MDCT) of the paranasal sinuses at King Fahd University Hospital between July 2018 and 2023 for different indications. Radiological variables analyzed included sphenoid sinus pneumatization type, presence and number of inter-sphenoid septa, and deviation or attachment to adjacent structures. Results: The data of 2433 patients were analyzed (44.5% males, 55.5% females; mean age 40 ± 15 years). The mean sphenoid sinus volume was 20.4 ± 8.7 cm3, significantly larger in males (p < 0.001). The most common sinus shape was quadrilateral (33%), whereas the predominant pneumatization pattern was post-sellar (57.1%), followed by sellar (32.1%), pre-sellar (9.2%), and conchal (1.6%). Adjacent-structure pneumatization was frequent, most notably in the greater wing of the sphenoid (47.4%) and pterygoid (39%) processes. Optic-canal protrusion and dehiscence were observed in 13.9% and 4.1%, respectively, whereas carotid canal protrusion occurred in 22.2% and dehiscence in 3.2%. Intra-sinus septation was identified in 97.7% of assessable cases, most commonly as a single septum (59.6%). Several variants showed significant sex-related associations, including sinus volume, anterior clinoid process/posterior clinoid process pneumatization, and dehiscence patterns. Conclusions: CT imaging revealed considerable diversity in the sphenoid-sinus anatomy among the Saudi population. Awareness of these variations, particularly their relationship with critical neurovascular structures, is crucial for radiologists and surgeons to ensure accurate diagnosis and safe surgical planning.

Tomography doi: 10.3390/tomography12020025

Authors: Abdullah Bin Sawad Muhammad Binsawad

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology.

Tomography doi: 10.3390/tomography12020024

Authors: Betül Tiryaki Baştuğ Türkan Güney

Background: Acute abdominal emergencies represent a major diagnostic challenge in emergency medicine, requiring rapid and accurate integration of clinical, laboratory, and imaging data. Although laboratory parameters play a central role in real-world diagnostic workflows, the extent to which they are systematically represented and integrated within radiology research publications remains unclear. Objective: To evaluate how laboratory data are represented, contextualized, and functionally utilized in radiology publications focusing on computed tomography (CT)–based evaluation of acute abdominal emergencies. Methods: A methodological content analysis was conducted on 72 original radiology research articles published between 2020 and 2024. Eligible studies focused on CT-based imaging of acute abdominal emergency conditions. Publications were screened and analyzed at the title and abstract level using a predefined coding framework to assess the presence of laboratory data, types of laboratory parameters reported, their contextual role (background information, imaging trigger, diagnostic modifier, or prognostic indicator), degree of laboratory–imaging integration, and presence of decision-oriented reporting. Descriptive statistics were used to summarize reporting patterns. Results: Laboratory data were reported in 61.1% of all included studies (n = 44/72). However, their functional utilization varied substantially. Laboratory parameters were most frequently presented as background clinical information, whereas explicit use as imaging triggers (26.4%, n = 19/72), diagnostic modifiers (19.4%, n = 14/72), or components of explicit laboratory–imaging integration (15.3%, n = 11/72) was less common. Decision-oriented reporting was present in 23.6% of all studies (n = 17/72). Explicit integration was described in publications addressing diagnostically complex and time-sensitive conditions, such as acute bowel ischemia and severe acute pancreatitis. Conclusion: Laboratory data are commonly reported in CT-based radiology publications addressing acute abdominal emergencies; however, the manner in which these data are incorporated into imaging-centered diagnostic narratives varies across studies. Differences are observed in how laboratory–imaging relationships are described, with some publications presenting integrated discussion and others reporting imaging findings independently of laboratory context. These observations characterize reporting practices within the analyzed literature and do not imply statistical associations or causal effects.

Tomography doi: 10.3390/tomography12020023

Authors: Zhibiao Cheng Jun Zhang Ping Chen Junhai Wen

Objectives: Due to collimator limitations, Single-Photon Emission Computed Tomography (SPECT) suffers from relatively low spatial resolution, which hampers the detection of small lesions. This study proposes a super-resolution (SR) reconstruction algorithm for a parallel-beam, low-resolution (LR) multi-detector SPECT system and employs a neural network to estimate and correct for geometric errors in the LR detectors. Methods: A parallel-beam LR multi-detector SPECT system is presented, in which the detectors perform relative sub-pixel shifts. At each sampling angle, an SR reconstruction algorithm synthesizes high-resolution (HR) SPECT images from LR projections acquired by four offset LR detectors. To correct for geometric errors among these detectors, a randomly distributed gamma point source was designed to generate training data. A neural network was then employed to estimate the geometric errors, thereby refining the SR reconstruction. Results: Numerical simulation demonstrated that the proposed neural network could accurately identify the displacement-based geometric errors of the LR detectors. Utilizing these estimated parameters to correct the SR reconstruction process yielded results comparable to those obtained from direct reconstruction of HR projections, achieving a two-fold resolution improvement. Conclusions: Preliminary proof-of-principle for SR reconstruction in a parallel-beam LR multi-detector SPECT system was established. Further validation of the hardware performance is warranted.

Tomography doi: 10.3390/tomography12020022

Authors: Laura Maria Cacioppa Michaela Cellina Giacomo Agliata Francesco Mariotti Nicolo’ Rossini Tommaso Valeri Giangabriele Francavilla Alessandro Felicioli Alessandra Bruno Marzia Rosati Roberto Candelari Chiara Floridi

Background: Visceral artery aneurysms (VAAs) are rare but potentially life-threatening vascular lesions often clinically silent until rupture. The widespread use of advanced imaging has increased incidental detection, highlighting the need for accurate, noninvasive diagnostic strategies. Dual-Energy Computed Tomography Angiography (DECTA) offers potential advantages over conventional CT across diagnostic and post-treatment settings; however, its role in VAAs remains incompletely defined. This narrative review summarizes current evidence on DECTA applications in VAAs, focusing on diagnosis, emergency evaluation, and post-treatment follow-up. Methods: A non-systematic literature search of PubMed and Embase focusing on English-language articles up to June 2025 was performed. The search included peer-reviewed original research articles, systematic reviews, and meta-analyses addressing dual-energy CT and spectral CT in vascular and aneurysmal imaging. Case reports without technical data and non-English articles were excluded. Results: In the diagnostic phase, DECTA enhances tissue differentiation through virtual monoenergetic images, iodine maps, and material decomposition reconstructions. In the post-treatment setting, DECTA supports assessment after endovascular procedures, including coil embolization or stent graft placement. In VAAs, these techniques may improve aneurysm delineation, reduce metal artifacts after endovascular treatment, enable accurate detection of endoleaks or residual perfusion, and support volumetric follow-up. Virtual Non-Contrast images may reduce radiation exposure without compromising diagnostic confidence. Conclusions: DECTA represents a versatile imaging modality with potential benefits across the diagnostic, emergency, and post-treatment phases of VAA management. Although many applications are extrapolated from aortic and peripheral vascular disease, emerging evidence supports its growing clinical relevance. Further dedicated studies are needed to define its role in VAA-specific decision-making and follow-up.

Tomography doi: 10.3390/tomography12020021

Authors: Adil Aytaç Hilal Aydın

Objectives: To evaluate regional anatomical differences in brain volume, surface area, and cortical thickness between children with breath-holding spells (BHSs) and a control group using morphometric MRI analyses. Methods: Three-dimensional T1-weighted cranial MRI data from 48 children with BHSs and 50 control children were retrospectively analyzed, yielding volumetric, surface area, and cortical thickness measures for 135 brain regions. All measurements were assessed relative to total intracranial volume (ICV). Group comparisons were performed using analysis of covariance with age, sex, and ICV as covariates, followed by Benjamini–Hochberg false discovery rate correction (q < 0.05). Results: The BHS group exhibited reduced bilateral amygdala volumes (left: q = 0.042; right: q = 0.038). Both cortical thickness and volume were reduced in the right anterior insula (thickness: q = 0.046; volume: q = 0.049). In addition, cortical thickness was reduced in the bilateral anterior cingulate cortices (left: p = 0.019, q = 0.045; right: p = 0.017, q = 0.043) as well as in the right medial frontal cortex (p = 0.009, q = 0.036). Subregional cerebellar analysis demonstrated volume reductions in the right lobule VI (q = 0.031), left lobule VIIA (Crus I) (q = 0.043), and vermis IX–X (q = 0.039). Conclusions: Detecting measurable morphometric changes in brain regions involved in autonomic and emotional regulation in children with BHSs will contribute to understanding the neurobiological characteristics associated with BHSs.

Tomography doi: 10.3390/tomography12020020

Authors: Ali Albweady

Objectives: This retrospective, multi-center study analyzed pre-existing anonymized clinical data from electronic health records and imaging archives. The analysis utilized real-world clinical data from 200 patients across four tertiary care centers, without additional patient recruitment or interventions. This study aims to investigate the impact of metabolic and physiological factors—specifically blood glucose levels, cortisol concentrations, fasting duration, and tumor histology—on the quality and diagnostic reliability of 18F-FDG PET/CT imaging in patients with primary brain tumors and inflammatory lesions. Methods: A total of 200 patients with primary brain tumors (including astrocytoma, glioblastoma, meningioma, and oligodendroglioma) were evaluated across four institutions using standardized protocols. The study examined the effects of prolonged fasting (>12 h), hyperglycemia (>150 mg/dL), and strict fasting (4–6 h) on tumor-to-background contrast and visual analog scale (DQS) scores. Results: Prolonged fasting was associated with elevated cortisol levels (correlation +0.54, p < 0.001), while hyperglycemia significantly reduced tumor SUVmax by up to 20% (r = −0.35, p = 0.012). Strict fasting and glucose control resulted in improved tumor-to-background contrast and DQS scores (r = +0.83, p < 0.001). Glioblastomas exhibited the highest SUVmax (9.1 ± 3.5), indicating aggressive metabolic activity, whereas meningiomas showed elevated cortisol levels (20.5 ± 6.8 µg/dL) linked to disruption of the hypothalamic–pituitary axis. Regression analysis confirmed that both cortisol and glucose levels independently degraded image quality (β = −0.25 and −0.18, respectively; p < 0.05). Conclusions: The findings highlight the necessity for harmonized patient preparation protocols. Recommendations are in alignment with the SNMMI Procedure Standard/EANM Practice Guideline for Brain [18F] FDG PET imaging.

Tomography doi: 10.3390/tomography12020019

Authors: Veli Süha Öztürk Tubanur Şanlı Ali Balcı Onur Hapa

Background: This study aimed to investigate the association between femoroacetabular impingement (FAI) morphology and femoral head bone marrow edema of unknown etiology on hip magnetic resonance imaging (MRI), and to assess the added value of computed tomography-based three-dimensional maximum intensity projection (CT-MIP) measurements in identifying a predisposition to acetabular overcoverage. Methods: Hip MRI examinations performed between January 2007 and 2025 were retrospectively reviewed. Cases with bone marrow edema attributable to identifiable etiologies were excluded. Twenty-six patients with available hip or pelvis computed tomography (CT) examinations obtained within one year were included, along with an age- and sex-matched control group imaged for indications unrelated to hip pain. A total of 104 hip joints were evaluated. Alpha angles were measured on axial oblique CT reformations. Virtual pelvic radiographs generated from CT-based three-dimensional reconstructions were used for lateral center-edge angle (LCEA) measurements, and acetabular coverage was quantified using the acetabular coverage index derived from CT-MIP images. Appropriate statistical analyses were performed, with p < 0.05 considered statistically significant. Results: FAI was identified in 82.7% of cases with bone marrow edema of unknown etiology on MRI (p < 0.001), with pincer-type morphology being the most prevalent subtype (55.8%). Bone marrow edema was significantly more common in pincer-type FAI compared with other subtypes (p < 0.001) and predominantly involved the posterolateral femoral head. Mean alpha angle, LCEA, and acetabular coverage index values were significantly higher in the case group than in controls (p < 0.001). For the detection of pincer-type FAI, CT-MIP-based acetabular coverage index demonstrated superior diagnostic performance compared with LCEA (AUC, 0.917 vs. 0.855; p = 0.017), with an optimal cutoff value of 0.93 yielding high specificity and accuracy. All measurements showed excellent intraobserver and interobserver reliability. Conclusions: Femoral head bone marrow edema of unknown etiology may serve as a radiologic clue to underlying pincer-type FAI, while CT-MIP-based analyses may provide incremental value beyond conventional angular measurements in characterizing acetabular overcoverage.

Tomography doi: 10.3390/tomography12020018

Authors: Sam Sedaghat Jin Il Park Eddie Fu Youngkyoo Jung Hyungseok Jang

Background/Objectives: Tissue conductivity reflects ionic composition (e.g., sodium), providing critical insights into various diseases. Ultrashort echo time quantitative conductivity mapping (UTE-QCM) offers a method to obtain this information, which is particularly effective for musculoskeletal (MSK) tissues with short T2 relaxation times. The aim of this study is to develop a UTE-QCM framework using ultrashort echo time double echo steady-state (UTE-DESS) and validate its feasibility in the knee. Methods: An ultrashort echo time double echo steady-state (UTE-DESS) sequence was used to acquire S+ and S− images and estimate the transmit radiofrequency field (B1+) phase at 3T. The B1+ phase was derived by canceling the phase evolution in the free induction decay using these images. This phase data was then processed using two widely used QCM reconstruction methods for comparison: parabolic fitting and an integral-based method. The proposed UTE-QCM framework was validated using a phantom containing three different concentrations of sodium chloride (0%, 0.5%, and 1%). Additionally, three healthy volunteers were recruited to validate UTE-QCM in knee imaging. Results: In both phantom and in vivo experiments, the integral-based QCM demonstrated improved robustness to noise compared to parabolic fitting. In the sodium phantom, the estimated conductivity showed high linearity with sodium concentrations. In the in vivo knee, the generated conductivity maps successfully visualized both long and short T2 tissues. Conclusions: We demonstrated the feasibility of UTE-QCM as a novel quantitative imaging tool targeting short T2 tissues in the MSK system. This technique may facilitate the diagnosis and prognosis of joint disorders.

Tomography doi: 10.3390/tomography12020017

Authors: Sung Ui Shin Mijung Jang Bo La Yun Su Min Cho Yoon Yeong Choi Bohyoung Kim Min Jung Kim Sun Mi Kim

Background/Objectives: We compared the visibility of breast cancer using the newly developed standing automated breast ultrasound system (MammouS-N) and digital breast tomosynthesis (DBT), and identified factors influencing lesion visibility. Methods: We prospectively enrolled 100 women (mean age: 51.6 years; range: 26–76 years) who were diagnosed with breast cancer and were scheduled to undergo DBT between January and July 2024. They underwent DBT and an ultrasound on the same day. Two radiologists evaluated the visibility scores (0–5) of lesions corresponding to biopsy-confirmed breast cancers identified using magnetic resonance imaging. The Wilcoxon signed-rank test was used to compare the visibility scores of cancers identified on DBT and/or MammouS-N images. Results: Among the 100 women, invasive ductal carcinoma was the most common malignancy (73%). DBT findings included negative findings (7%), masses (46%), masses with calcification (29%), calcifications only (15%), and architectural distortions (3%). On MammouS-N ultrasound, most lesions were classified as masses (93%), whereas 7% were non-mass lesions. For Reviewer 1, MammouS-N demonstrated significantly higher visibility scores (higher scores: 26 on MammouS-N, seven on DBT; equal scores: 67, z = −3.234, p = 0.001). For Reviewer 2, the two modalities showed no significant difference in visibility (higher scores: 27 on MammouS-N, 28 on DBT, equal scores: 45, z = −0.040, p = 0.968). Noncalcified lesions that were obscured on DBT were better visualized on MammouS-N (p < 0.001) by both reviewers. Conclusions: MammouS-N holds promise as an imaging modality complementary to DBT in women with dense breast tissue, particularly for non-calcified lesion detection.

Tomography doi: 10.3390/tomography12020016

Authors: Betül Tiryaki Baştuğ Türkan Güney

Background: Emergency radiology often demands rapid integration of clinical cues, biochemical markers, and imaging findings to support time-critical diagnostic reasoning. However, educational resources that explicitly structure this interdisciplinary integration particularly between radiology and laboratory medicine remain limited. Objective: Our objective was to develop an Integrated Radiology–Biochemistry Diagnostic Flow Framework as a simulation-based methodological proof-of-concept and to document its structure, logic pathways, and internal consistency across common emergency presentations. Methods: We designed an algorithmic framework combining (i) clinical triggers, (ii) targeted biochemical markers with predefined threshold and trajectory rules, (iii) imaging indication and modality selection (US/CTA/MRI/NCCT), and (iv) key radiologic patterns linked to escalation pathways. No patient data or human participants were included. Instead, forty fully synthetic emergency scenarios were generated to populate the framework and to examine logical completeness, branching coherence, and red-flag escalation routes. Results: The framework yielded scenario-specific diagnostic flowcharts that systematically connect biochemical escalation cues with imaging selection and expected imaging findings. The synthetic scenario library demonstrated consistent branching logic across conditions and enabled transparent visualization of imaging-centered decision pathways suitable for simulation-based teaching and structured case discussion. Conclusions: This study reports a reproducible methodological proof-of-concept framework and a synthetic emergency scenario library. Further learner-based studies are required to evaluate usability, perceived realism, and educational effectiveness in authentic training settings.

Tomography doi: 10.3390/tomography12020015

Authors: Ercan Ayaz Irem Kavukoglu Nazli Gulsum Akyel

Background: We aimed to determine paranasal sinus volumes using 3D volumetric measurements and to evaluate the effect of anatomical variations on these volumes, ensuring balanced age and sex distribution during childhood. Methods: Thirteen age groups (0–16 years), each including 10 males and 10 females, were formed. After excluding sinus pathologies, a total of 260 subjects were randomly selected from CT head examinations. Right and left frontal, maxillary, and sphenoid sinus volumes were calculated using 3D Slicer software (version 5.6.2) following manual segmentation of axial CT slices. Also, the presence of right and left Agger Nasi cells, Haller cells, Onodi cells, and concha bullosa were recorded. Results: No significant difference was found between males and females in sinus volumes (p > 0.05). Mean right and left maxillary sinus volumes were 6.23 cm3 and 6.27 cm3 (p = 0.551); frontal sinuses were 0.79 cm3 and 0.86 cm3 (p = 0.170); and sphenoid sinuses were 1.64 cm3 and 1.85 cm3 (p = 0.041). Sphenoid sinus pneumatization appeared in 30% of the 0–6-month group and in over 75% of older groups. Frontal pneumatization began at age 2–3 and exceeded 50% after age 4. Agger Nasi, Haller, Onodi cells, and concha bullosa were detected in 58.8%, 31.2%, 10%, and 22.3% of cases, respectively. Anatomical variations showed no significant effect on sinus volumes (p > 0.05). Conclusions: We developed a paranasal sinus volume chart applicable to routine practice, showing that anatomical variations had no significant impact on the development. This is the first study to investigate the impact of anatomical variations on sinus development and volume, along with the age at which variations emerge, with a balanced distribution of age and sex.

Tomography doi: 10.3390/tomography12020014

Authors: Mustafa Demir Yunus Yasar

Objective: The number of operators performing mechanical thrombectomy (MT) may influence procedural outcomes; however, evidence remains limited and conflicting. This study aimed to comprehensively evaluate the impact of single versus dual operators on procedure time, radiation dose, and angiographic success in patients undergoing MT for acute ischemic stroke. Methods: In this single-center, retrospective cohort study, 285 consecutive patients who underwent MT for large-vessel occlusion between January 2020 and December 2024 were included. Patients were grouped according to institutional workflow: single-operator procedures (n = 157) and dual-operator procedures (n = 128). The primary endpoints were procedure time and radiation dose parameters, including total Kerma-Area Product (PKA). Secondary endpoints included successful reperfusion (TICI ≥ 2b), complete reperfusion (TICI 3), and first-pass success (FPS, defined as TICI 2c/3 with a single pass). Results: Baseline characteristics were comparable between groups. The dual-operator group had significantly shorter median procedure times (52.5 vs. 85.0 min, p < 0.001) and lower total PKA (p < 0.001). Reperfusion rates were significantly higher in the dual-operator group, both for successful reperfusion (TICI ≥ 2b: 80.5% vs. 64.3%, p = 0.004) and complete reperfusion (TICI 3: 76.6% vs. 58.5%, p = 0.002). First-pass success was also more frequent (60.0% vs. 44.5%, p = 0.0146), and the mean number of passes was lower (1.66 vs. 2.00, p = 0.0057). Conclusions: Mechanical thrombectomy performed with two experienced operators was associated with greater procedural efficiency, reduced patient radiation exposure, and higher angiographic success compared with single-operator procedures. These findings support considering the dual-operator model as an approach that may inform workforce planning and workflow decisions in stroke centers.

Tomography doi: 10.3390/tomography12020013

Authors: Alexander von Hessling Chloé Sieber Maria Blatow Christian Berner Dirk Lehnick Frauke Kellner-Weldon

Background/Objectives: This study evaluated the clinical image quality of three-dimensional synthetic MRI (3D SI) compared with conventional MRI (cMRI), focusing on tissue contrast, anatomical detail, and motion sensitivity. Methods: Patients with nonspecific neurological symptoms were included. Both cMRI and 3D SI were acquired on single-vendor 1.5 T and 3 T scanners with slice thicknesses of 1.0–1.7 mm. Two experienced neuroradiologists and one fellow independently evaluated matched scans using a 0–100 scale. Assessed parameters included signal-to-noise ratio (SNR), gray–white matter contrast, artifacts, motion robustness, and confidence in detecting perivascular spaces, white matter lesions, and subtle pathology. Interrater agreement was measured using Krippendorff’s alpha and ICC2. Multiple linear regression analyzed associations between image quality ratings and imaging method. Results: Images of 31 patients were analyzed. Three-dimensional SI demonstrated sufficient-to-good overall image quality and high robustness to motion. Cortical-surface-to-cerebrospinal-fluid contrast on FLAIR was rated lower for 3D SI than for cMRI. False-positive lesion detection occurred more frequently on 3D SI FLAIR, particularly among experienced readers. cMRI achieved significantly higher T1-weighted SNR than 3D SI (8.76 points, p < 0.001). Experienced readers consistently rated SNR and tissue contrast higher than the fellow. Vascular signal range was broader on 3D SI, reducing sensitivity to vascular abnormalities. Conclusions: Three-dimensional synthetic MRI provides clinically usable image quality and fulfills its primary diagnostic purpose, offering advantages in acquisition efficiency and robustness to motion. Nevertheless, limitations in cortical contrast, vascular signal characterization, and reader-dependent interpretive variability constrain its reliability for subtle or detail-critical findings.

Tomography doi: 10.3390/tomography12010012

Authors: Semih Sağlık Ayfer Ertekin

Background/Objectives: This study aimed to investigate the relationship between carotid artery anatomy and geometry and white matter hyperintensities (WMH) and to determine whether it is a risk factor for the disease. Methods: The geometry and anatomy of both carotid arteries were evaluated with the three-dimensional vessel model obtained from the computed tomography angiography (CTA) data, and the segmentation software calculated the geometrical features of the arteries. In this model, vascular diameter, vascular cross-sectional area, carotid bifurcation and internal carotid artery (ICA) angles, as well as ICA tortuosity index (TI) measurements of the common carotid artery (CCA) and ICA were determined. Results: Compared with the non-WMH group, increased carotid bifurcation and ICA angle and higher ICA TI values were found in the WMH group (p < 0.001). In multivariate regression analysis, increased carotid bifurcation angle, higher ICA TI values, age, hypertension, and stroke history were identified as independent risk factors for the development of WMH (p < 0.05). In addition, age, carotid bifurcation angles and ICA angles were found to be associated with the severity of WMH (p < 0.05). Conclusions: Considering the vascular pathologies involved in the pathogenesis of WMH, identifying these risk factors may help determine individuals who are at an increased risk.

Tomography doi: 10.3390/tomography12010011

Authors: Maher Dhanani Dominika Skwierawska Tristan Anselm Kuder Sabine Ohlmeyer Michael Uder Sebastian Bickelhaupt Frederik Bernd Laun

Background/Objectives: Diffusion-weighted imaging (DWI) is a magnetic resonance technique used to map the apparent diffusion coefficient (ADC) of water in human tissue. ADC assessment plays a central role in clinical diagnostics, as malignant tissues typically exhibit reduced water mobility and, thus, lower ADC values. Accurately measuring the ADC requires effective fat suppression to prevent contamination from the residual fat signal, which is commonly believed to cause ADC underestimation. This study aimed to demonstrate that ADC overestimation may occur as well. Methods: Our theoretical analysis shows that out-of-phase conditions between fat and water signals lead to ADC overestimations. We performed demonstration experiments on fat–water phantoms and the breasts of 10 healthy female volunteers. In particular, we considered three out-of-phase conditions: First and second, short-time inversion recovery (STIR) fat suppression with incorrect inversion time and incorrect flip angle, respectively. Third, phase differences due to spectral fat saturation. The ADC values were assessed in regions of interest (ROIs) that included both water and residual fat signals. Results: In the phantoms and the volunteer data, ROIs containing both fat and water signals consistently exhibited lower ADC values under in-phase conditions and higher ADC values under out-of-phase conditions. Conclusions: We demonstrated that out-of-phase conditions can result in ADC overestimation in the presence of residual fat signals, potentially resulting in false-negative classifications where malignant lesions are misinterpreted as benign due to an elevated ADC. Out-of-phase fat and water signals might also reduce lesion conspicuity in high b-value images, potentially masking clinically relevant findings.

Tomography doi: 10.3390/tomography12010010

Authors: Dana Sandra Daniel Mila Goldenberg Leonid Kalichman

Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI) serves as a visual biofeedback tool, providing real-time imaging to enhance PFM training, motor learning, and treatment adherence. Aim: This narrative review evaluates the role and efficacy of RUSI in pelvic floor rehabilitation. Method: A comprehensive search of PubMed, Cochrane, and MEDLINE was conducted using keywords related to pelvic floor rehabilitation, ultrasound, and biofeedback, limited to English-language publications up to July 2025. Systematic reviews, meta-analyses, and clinical trials were prioritized. Results: Transperineal and transabdominal ultrasound improve PFM function across diverse populations. In post-prostatectomy men, transperineal ultrasound-guided training enhanced PFM contraction and reduced urinary leakage. In postpartum women with pelvic girdle pain, transabdominal ultrasound-guided biofeedback combined with exercises decreased pain and improved function. Ultrasound-guided pelvic floor muscle contraction demonstrated superior performance compared to verbal instruction. Notably, 57% of participants who were unable to contract the pelvic floor muscles with verbal cues achieved a correct contraction with ultrasound biofeedback, and this approach also resulted in more sustained improvements in PFM strength. Compared to other biofeedback modalities, RUSI demonstrated outcomes that are comparable to or superior to those of alternative methods. However, evidence is limited by a lack of standardized protocols and randomized controlled trials comparing RUSI with other modalities. Conclusions: RUSI is an effective visual biofeedback tool that enhances outcomes of PFM training in pelvic floor rehabilitation. It supports clinical decision-making and patient engagement, particularly in cases where traditional assessments are challenging. Further research, including the development of standardized protocols and comparative trials, is necessary to optimize the clinical integration of this method and confirm its superiority over other biofeedback methods.

Tomography doi: 10.3390/tomography12010009

Authors: Ömer Demir Kamil Serkan Ağaçayak

Background: The pterygomaxillary region is a complex anatomical area formed by the junction of the maxillary, palatine, and sphenoid bones and contains critical neurovascular structures. Accurate assessment of this region during Le Fort I osteotomy is essential, particularly to prevent hemorrhage and nerve injury that may occur during the pterygomaxillary separation phase. This study aims to investigate the morphometric characteristics of the pterygomaxillary region using cone-beam computed tomography (CBCT) and to evaluate the effects of age, sex, and laterality on these anatomical parameters. Materials and Methods: In this retrospective study, CBCT scans of 200 individuals (100 males and 100 females) aged 20–80 years were analyzed. Axial measurements included distances between the piriform rim, the descending palatine artery, the pterygomaxillary osteotomy line, and the pterygomaxillary fissure. Additionally, the thickness and width of the pterygomaxillary region and pterygoid process, lengths of the medial and lateral pterygoid laminae, and the distance between the greater palatine canal and the medial pterygoid lamina apex were recorded. Measurements were statistically evaluated by sex, age group, and laterality. Results: The following parameters demonstrated statistically significant differences based on the conducted measurements: The distance between the piriform rim and the descending palatine artery was significantly greater on the left side (p < 0.001). The length of the lateral pterygoid lamina increased with advancing age (p = 0.048). The thickness of the pterygomaxillary region was significantly greater in females (p = 0.014). Additionally, the distance between the greater palatine canal and the terminal point of the medial pterygoid lamina was significantly higher in males (p < 0.001). Conclusions: The pterygomaxillary region exhibits anatomical variations that may lead to serious complications during Le Fort I osteotomy. Detailed preoperative evaluation of this area using CBCT can guide surgical planning and help prevent potential vascular and neural complications.

Tomography doi: 10.3390/tomography12010008

Authors: Lena Supe Stefania Rizzo

Background/Objectives: Pancreatic cancer is among the most aggressive malignancies, with poor survival rates. Emerging evidence suggests that body composition, including skeletal muscle mass and adiposity distribution, plays a crucial role in predicting patient outcomes. However, its impact on survival in pancreatic cancer remains incompletely understood. The aim of this systematic review was to assess the correlation between body composition parameters and survival outcomes in pancreatic cancer patients, focusing on overall survival. Methods: A comprehensive literature search was conducted, including three main components: pancreatic cancer, body composition, and survival outcomes. Results: 23 studies were included in this review. The findings indicate that body composition can serve as a predictor of survival in pancreatic cancer patients, with 21 studies reporting a significant correlation. The most frequently observed predictor, with 11 studies reporting, was not a baseline parameter but rather changes in parameters over time during treatment. However, discrepancies remain regarding the extent of predictive power and the relative importance of individual components. Conclusions: Specific body composition parameters hold potential as prognostic indicators of survival in pancreatic cancer patients. However, further research is necessary to establish consistent patterns and to clarify which parameters are most predictive and under what conditions.

Tomography doi: 10.3390/tomography12010007

Authors: Yusuke Kobayashi Yuki Tanabe Tomoro Morikawa Kazuki Yoshida Kentaro Ohara Takaaki Hosokawa Takanori Kouchi Shota Nakano Osamu Yamaguchi Teruhito Kido

Background/Objectives: Super-resolution deep-learning reconstruction (SR-DLR) is an advanced image reconstruction technique, but its effect on dynamic myocardial computed tomography perfusion (CTP) imaging has not been evaluated. This study aimed to examine the impact of SR-DLR on image quality and perfusion parameters in dynamic myocardial CTP. Methods: Thirty-five patients who underwent dynamic myocardial CTP for coronary artery disease assessment were retrospectively analyzed. Two CTP datasets were reconstructed using hybrid iterative reconstruction (HIR) and SR-DLR. Image quality was compared qualitatively and quantitatively, including image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge rise slope (ERS). Equivalence of CT-derived myocardial blood flow (CT-MBF) between two reconstructions was tested using a previously reported 15% equivalence margin. Intra-patient variability of CT-MBF was evaluated using the robust coefficient of variation (rCV). Results: In the qualitative assessment, SR-DLR had significantly higher scores in contrast (4.0 vs. 2.0) and sharpness (4.5 vs. 2.5) compared with HIR (p < 0.001), while contrast scores were similar. In the quantitative assessment, SR-DLR demonstrated significantly lower image noise (19.4 vs. 29.4 HU), and improved SNR (6.1 vs. 4.1), CNR (13.7 vs. 10.9), and ERS (171.0 vs. 135.1 HU/mm) (all p < 0.001). Mean global CT-MBF was comparable (3.15 ± 0.91 mL/g/min for HIR vs. 3.18 ± 0.97 mL/g/min for SR-DLR) and equivalence was confirmed (p = 0.022). SR-DLR significantly reduced rCV compared with HIR (36.0% vs. 41.0%, p < 0.001). Conclusions: SR-DLR enhances image quality in dynamic myocardial CTP while maintaining mean global CT-MBF and reducing intra-patient variability.

Tomography doi: 10.3390/tomography12010006

Authors: Maya Honda Masako Kataoka Mami Iima Rie Ota Akane Ohashi Ayami Ohno Kishimoto Kanae Kawai Miyake Marcel Dominik Nickel Yosuke Yamada Masakazu Toi Yuji Nakamoto

This correction addresses several errors identified in the original publication [...]

Tomography doi: 10.3390/tomography12010005

Authors: Yusuke Inoue Hiroyasu Itoh Hirofumi Hata Kei Kikuchi

Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software. Methods: A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters. Results: The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version. Conclusions: Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions.

Tomography doi: 10.3390/tomography12010004

Authors: Muhammad Zaeem Khalid Nida Iqbal Babar Ali Jawwad Sami Ur Rahman Saman Iqbal Lama Almudaimeegh Zuhal Y. Hamd Awadia Gareeballah

Background/Objectives: Alzheimer’s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI). Methods: Four ML classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were trained on demographic and clinical features. For stage-wise classification, five DL models—CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2—were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations. Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.

Tomography doi: 10.3390/tomography12010003

Authors: Gulten Taskin Muzaffer Elmali Aydin Deveci Irem Ceren Koc

Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including CD4 lymphocyte count and plasma viral load, in HIV-positive patients. Materials and Methods: In this retrospective study, 113 HIV-positive patients who underwent contrast-enhanced chest CT were included. Patients were stratified by CD4 count (<200, 200–500, >500 cells/μL) and plasma viral load (<100,000 or >100,000 copies/mL). Axillary lymph node parameters—including maximum and minimum diameters, cortical thickness, hilar width, and density (Hounsfield units, HU)—were measured on multiplanar reconstructed CT images. Group differences were assessed using the Kruskal–Wallis and Mann–Whitney U tests, and Spearman’s correlation was used to evaluate associations between imaging and laboratory findings. Receiver operating characteristic (ROC) curve analysis identified optimal density thresholds. Results: Lymph node diameters, cortical thickness, and hilar width did not significantly differ between CD4 groups. However, mean lymph node density was higher in patients with CD4 < 200 cells/μL (p = 0.024). A density threshold of 84.5 HU distinguished impaired from preserved immune function (sensitivity 61.1%, specificity 71.2%). Patients with viral load >100,000 copies/mL showed increased lymph node density, minimal diameter, and cortical thickness. Conclusions: Elevated axillary lymph node density correlates with immune suppression and high viral load, suggesting its potential as a non-invasive prognostic imaging biomarker in HIV infection.

Tomography doi: 10.3390/tomography12010002

Authors: Rasha Rudaid Alharthi Duaa Banaja Adnan Alahmadi Jaber Hussain Alsalah Arwa Baeshen Ali H. Alghamdi Magbool Alelyani Njoud Aldusary

Objective: This study aimed to investigate the functional connectivity (FC) of three amygdala subregions—the laterobasal amygdala (LBA), centromedial amygdala (CMA), and superficial amygdala (SFA)—with large-scale brain networks in individuals with schizophrenia (SCZ) compared to healthy controls (HC). Methodology: Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 100 participants (50 SCZ, 50 HC) with balanced age and gender distributions. FC between amygdala subregions and target functional networks was assessed using a region-of-interest (ROI)-to-ROI approach implemented in the CONN toolbox. Result: Connectivity patterns of the LBA, CMA, and SFA differed between SCZ and HC groups. After false discovery rate (FDR) correction (p < 0.05), SCZ patients exhibited significantly increased FC between the left CMA and both the default mode network (DMN) and the visual network (VN). In contrast, decreased FC was observed between the right LBA and the sensorimotor network (SMN) in SCZ compared with HC. Conclusions: These findings reveal novel FC alterations linking amygdala subregions with large-scale networks in schizophrenia. The results underscore the importance of examining the amygdala as distinct functional subregions rather than as a single structure, offering new insights into the neural mechanisms underlying SCZ.

Tomography doi: 10.3390/tomography12010001

Authors: Emilio Quaia

The scientific publishing crisis represents a complex problem, mainly stemming from the “publish or perish” culture that prioritizes quantity over quality, which leads to the proliferation of low-quality research manuscripts and research misconduct, including data fabrication (making up data or results), falsification (manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record), or even plagiarism (the appropriation of another person’s ideas, processes, results, or words without giving appropriate credit) [...]

Tomography doi: 10.3390/tomography11120143

Authors: Neda Nilforoushan Ashkan Khavaran Maierdan Palihati Yashvi Patel Anna O. Giarratana Jeeban Paul Das Kathleen M. Capaccione

Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting immune evasion, and supporting angiogenesis and metastasis. Fibroblast activation protein (FAP) is a transmembrane serine protease minimally expressed in normal adult tissues but significantly upregulated in certain subtypes of CAFs across many solid tumors. High levels of FAP have been associated with poor prognosis in various cancers. FAP has increasingly emerged as a promising target for both imaging and therapy. Multiple FAP-targeting strategies, such as small molecules, monoclonal antibodies, drug conjugates, and radiolabeled ligands, are currently being investigated in preclinical and early clinical settings. This review provides a clinically focused overview of CAFs in the tumor microenvironment, highlighting key fibroblast markers, their associations with prognosis across various tumor types, and their utility in radiologic imaging and targeted therapy. We also discuss the potential of non-FAP fibroblast targeting molecules and the clinical rationale for more selective, subtype-specific strategies. By examining fibroblast biology through a radiologist’s lens, we aim to explore the evolving role of stromal targeting in imaging and the treatment of solid tumors.

Tomography doi: 10.3390/tomography11120142

Authors: Sevgi Ünal Remzi Gürfidan Merve Gürsoy Bulut Mustafa Fazıl Gelal

Background/Objectives: The breast is a highly radiosensitive organ that is directly exposed to ionizing radiation during chest computed tomography (CT) examinations. Excessive radiation exposure increases the risk of radiation-induced malignancies, highlighting the importance of accurate and patient-specific dose estimation. This study aims to estimate the effective radiation dose absorbed by the breast during chest CT examinations using a machine learning (ML)-based personalized prediction approach. Methods: In this retrospective study, a total of 653 female patients who underwent both mammography and chest CT between 2020 and 2024 were included. A structured database was created incorporating demographic and anatomical parameters, including body weight, height, body mass index (BMI), and breast thickness (mm) obtained from mammography, along with dose length product (DLP) values from chest CT scans. Five regression-based ML algorithms—CatBoost, Gradient Boosting, Extra Trees, AdaBoost, and Random Forest—were implemented to predict breast radiation dose. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R2). Results: Among the evaluated models, the CatBoost algorithm optimized with Particle Swarm Optimization (CatBoostPSO) achieved the best overall predictive performance, yielding the lowest MSE (0.3795), MAE (0.3846), and MAPE (4.37%), along with the highest R2 value (0.9875). CatBoost and Gradient Boosting models demonstrated predictions most closely aligned with ground truth values, indicating that ensemble-based and dynamically optimized models are particularly effective for breast dose estimation. Conclusions: The proposed machine learning framework enables rapid, accurate, and clinically applicable estimation of breast radiation dose during chest CT examinations. This patient-specific approach has strong potential to support personalized radiation dose monitoring and optimization strategies, contributing to improved radiation safety in clinical practice.

Tomography doi: 10.3390/tomography11120141

Authors: Mattias Drake Emma Hall Birgitta Ramgren Björn M. Hansen Johan Wassélius

Background: Accurate volumetry and imaging characterization of chronic subdural hematoma (cSDH) are essential for prognostication and treatment planning, but manual assessment is time-consuming and therefore underutilized. Methods: We retrospectively analyzed preoperative non-contrast CT (NCCT) scans of 257 patients undergoing first-time surgery for uni- or bilateral cSDH. Hematoma volumes were measured manually using a semi-automated area-outlining tool on every second axial slice and compared with the volumes estimated through the ABC/2 formula. Hematoma attenuation patterns and components were categorized, and interrater reliability was assessed for volume, maximum diameter, and imaging features using intraclass correlation coefficients (ICCs) and Cohen’s κ. Results: A total of 339 hematomas were evaluated. Manual and ABC/2 volume measurements correlated strongly (R2 = 0.83, ICC [3, 1] = 0.90). The interrater agreement for manual volumetry was excellent (ICC [2, 1] = 0.96). Agreement was also excellent for maximum diameter (ICC [2, 1] > 0.9) and good for midline shift assessment (κ = 0.81). Agreement was moderate for the identification of fresh clots, trabeculations, and laminations (κ = 0.62–0.72) but poor for general attenuation patterns (κ = 0.44). Conclusions: The manual volumetry of cSDH is feasible and highly reproducible between raters of different experience levels. These results provide a robust reference standard for the validation of automated volumetry tools and support the implementation of quantitative hematoma assessment in future clinical trials and routine care.

Tomography doi: 10.3390/tomography11120140

Authors: Sang Won Park Doohee Lee Jae Eun Song Yoon Kim Hyun-Soo Choi Seung-Joon Lee Woo Jin Kim Kyoung Min Moon Oh Beom Kwon

Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes, remains insufficiently characterized. Methods: We conducted a pilot evaluation of a previously developed DL model using 135 chest radiographs from Kangwon National University Hospital. Expert physicians established the reference standard. Model performance was assessed and receiver operating characteristic (ROC) curve and precision recall curve (PRC) analyses were performed. Differences between correctly classified and misclassified cases were examined using Wilcoxon rank-sum and Fisher’s exact tests to explore potential clinical or radiographic contributors to model failure. Results: The model correctly classified 129 of 135 cases. The sensitivity was 96.1% (95% confidence interval (CI): 92.2–98.9%), specificity was 85.7% (95% CI: 42.2–97.7%), positive predictive value (PPV) was 99.2% (95% CI: 96.1–99.9%), negative predictive value (NPV) was 54.5% (95% CI: 25.4–80.8%), balanced accuracy was 90.8%, and F1-score was 0.976. The area under the ROC curve was 0.970 (95% CI: 0.929–1.000) and that under the PRC was 0.727 (95% CI: 0.289–1.000), reflecting substantial uncertainty related to the very small number of incomplete cases (n = 6). No statistically significant differences in clinical or radiographic characteristics were observed between correctly classified and misclassified cases. Conclusions: The DL model performed well in identifying correctly positioned NG tubes but demonstrated limited and unstable performance for detecting incomplete placements. Given the safety implications of misclassification, the model should be used only as an assistive tool with mandatory physician oversight. Larger, multi-center studies with greater representation of incomplete cases are required to obtain more reliable estimates and support safe clinical implementation.

Tomography doi: 10.3390/tomography11120139

Authors: Mandeep Singh Amirhossein Moaddab Doosup Shin Jonathan Weber Karen Chau Ali H. Dakroub Roosha Parikh Karli Pipitone Ziad A. Ali Omar K. Khalique

Background/Objectives: Aortic valve calcification is commonly evaluated using 3.0 mm true non-contrast (TNC) computed tomography (CT) images. This study evaluates the reproducibility of virtual non-contrast (VNC) reconstructions at different slice intervals using photon-counting detector CT (PCD-CT). Methods: In this retrospective study, we included 279 consecutive patients, who underwent PCD-CT for evaluation of native aortic valve between February 2023 and December 2023 with both TNC and VNC images at 3.0 and 1.5 mm slice intervals. Aortic valve calcium score (AVCS) and aortic valve calcium volume (AVCV) were compared between the two methods using paired t-tests. Agreement for continuous variables was assessed using inter-class coefficients (ICCs). Cohen’s Kappa (κ) was calculated to evaluate the agreement between different modalities in diagnosing severe AV calcification. Results: Compared to the standard, TNC images at 1.5 mm intervals showed higher AVCS (mean difference: −290 ± 418, p < 0.001), with high reproducibility between techniques (CS: ICC 0.969, [IQR 0.962, 0.975]). Compared with reference, VNC showed no significant differences in AVCS at either slice intervals, with excellent reproducibility (3.0 mm, ICC 0.970 [0.963, 0.976]; 1.5 mm, ICC 0.971 [0.964, 0.977]). Compared to TNC 3.0 mm, strong concordance was observed using other reconstruction techniques in assessing severe AV calcification (κ = 0.81 [95% CI: 0.74–0.88], 0.83 [95% CI: 0.76–0.90], and 0.83 [95% CI: 0.76–0.90] for TNC at 1.5 mm, VNC at 3.0 mm, and 1.5 mm, respectively), with low misclassification rates. Conclusions: Our study highlights high reproducibility in the evaluation of AVCS by VNC reconstruction at 3.0 and 1.5 mm intervals compared with reference offering a reliable alternative with an excellent diagnostic accuracy.

Tomography doi: 10.3390/tomography11120138

Authors: Zeki Ogut Mucahit Karaduman Muhammed Yildirim

Background/Objectives: Breast cancer is one of the most common malignancies in women worldwide. Early diagnosis and accurate classification in breast cancer detection are among the most critical factors determining treatment success and patient survival. In this study, a deep learning-based model was developed that can classify benign, malignant, and normal breast tissues from ultrasound images with high accuracy and achieve better results than the methods commonly used in the literature. Methods: The proposed model was trained on a dataset of breast ultrasound images, and its classification performance was evaluated. The model is designed to effectively learn both local textural features and global contextual relationships by combining Squeeze-and-Excitation (SE) blocks, which emphasize channel-level feature importance, and Convolutional Block Attention Module (CBAM) attention mechanisms, which focus on spatial information, with the MHA structure. The model’s performance is compared with three commonly used convolutional neural networks (CNNs) and three Vision Transformer (ViT) architectures. Results: The developed model achieved an accuracy rate of 96.03% in experimental analyses, outperforming both the six compared models and similar studies in the literature. Additionally, the proposed model was tested on a second dataset consisting of histopathological images and achieved an average accuracy of 99.55%. The results demonstrate that the model can effectively learn meaningful spatial and contextual information from ultrasound data and distinguish different tissue types with high accuracy. Conclusions: This study demonstrates the potential of deep learning-based approaches in breast ultrasound-based computer-aided diagnostic systems, providing a reliable, fast, and accurate decision support tool for early diagnosis. The results obtained with the proposed model suggest that it can significantly contribute to patient management by improving diagnostic accuracy in clinical applications.

Tomography doi: 10.3390/tomography11120137

Authors: Bo La Yun Sun Mi Kim Sung Ui Shin Su Min Cho Yoon Yeong Choi Mijung Jang

Background/Objectives: To identify preoperative MRI features using computer-assisted diagnosis (CAD) that are associated with invasive disease-free survival (IDFS) and distant metastasis-free survival (DDFS) in patients with primarily operable triple-negative breast cancer (TNBC). Methods: This retrospective study was approved by the institutional review board with informed consent was waived. Between January 2012 and December 2014, 74 consecutive women with primary TNBC (mean age, 51 years; range, 29–77 years) who underwent preoperative MRI were included and followed until August 2021. Dynamic contrast-enhanced and T2-weighted images were obtained using 3T scanners. Peritumoral edema and central necrosis were evaluated retrospectively. CAD was used to extract 3D diameters, angiovolume, and kinetic parameters, and kinetic heterogeneity was calculated. Cox proportional hazards models were used to assess associations between MRI features and IDFS and DDFS, adjusting for clinicopathologic factors. Results: During a median follow-up of 80.9 months, 12 patients developed invasive disease, and 8 developed distant metastasis. In multivariable analysis, peak enhancement (hazard ratio [HR], 1.40; 95% confidence interval [CI], 1.06–1.84; p = 0.019) and angiovolume (HR, 2.86; 95% CI, 1.26–6.47; p = 0.012) were independently associated with IDFS, whereas angiovolume (HR, 2.47; 95% CI: 1.28–4.78; p = 0.007) was independently associated with DDFS. Conclusions: Preoperative CAD-derived MRI features, particularly peak enhancement and angiovolume, were associated with IDFS in TNBC patients whereas angiovolume alone was associated with DDFS.

Tomography doi: 10.3390/tomography11120136

Authors: Sydney Eierle Tanja Taivassalo Hyunjun Park Korey D. Cooke Zahra Moslemi Sean C. Forbes Glenn A. Walter Krista Vandenborne S. H. Subramony Donovan J. Lott

Introduction: Myotonic dystrophy type 1 is the most prevalent muscular dystrophy in adults, characterized by weakness, impaired functional abilities, and myotonia. However, little is known about the relationship between quantitative MRI measures (fat fraction and T2 relaxation time) and clinical findings of the upper extremity. This study assessed forearm muscle structure in patients with myotonic dystrophy using quantitative MRI and correlated these measures with strength, function, and handgrip myotonia. Materials and Methods: Eighteen adults with myotonic dystrophy type 1 underwent MRI using three-point Dixon and T2 spin echo imaging of the forearm. Results: The average fat fraction and T2 relaxation time were greatest in the flexor digitorum profundus (26.7% and 55.6 ms, respectively). Correlations were found between quantitative MRI values and clinical tests of strength (r = −0.61 to −0.92, p < 0.01), function (r = −0.64 to −0.83, p < 0.01), and handgrip myotonia (r = 0.48, p < 0.05). Overall, the anterior forearm fat fraction values showed higher correlations with strength and function compared to those of the posterior forearm. Discussion: Our results support the use of quantitative MRI measures to assess forearm disease pathology and show potential to monitor the effectiveness of therapeutic treatments in patients with myotonic dystrophy type 1.

Tomography doi: 10.3390/tomography11120135

Authors: Tobias Norajitra Christopher L. Schlett Ricarda von Krüchten Prerana Agarwal Ashis Ravindran Thuy Duong Do Lisa Kausch Stefan Karrasch Hans-Ulrich Kauczor Klaus Maier-Hein Claudius Melzig

Background/Objectives: To assess diagnostic accuracy of two-dimensional (2D) projection methods for rapid visual quality control of automated volumetric (3D) lung segmentations compared with slice-based 3D review of segmentation results for application in large-scale studies. Methods: Segmentation of right and left lungs on T1-weighted MRI of 300 participants of the German National Cohort (NAKO) study was performed using the nnU-NET framework. Three variants of 2D projection images of segmentation masks were created: maximum intensity projection (MIP) using pseudo-chromadepth encoding with different color spectra for right and left lung (Colored_MIP) and standard deviation projection of segmentation mask outlines, encoded in black-and-white (Gray_outline) or using color-encoding (Colored_outline). The worst of two ratings by two independent raters conducting slice-based review for segmentation errors on underlying imaging data and review for mislabeling errors served as the standard of reference. All variants were evaluated by five raters each for identification of segmentation errors and the majority rating was used as index test. The time required for review was recorded and diagnostic accuracies were calculated. Results: Sensitivities of Colored_MIP, Colored_outline and Gray_outline were 88.2% [95%-CI 78.7%; 94.4%], 89.5% [80.3%; 95.3%] and 78.9% [68.1%; 87.5%]; specificities were 98.7% [96.1%; 99.7%], 96.4% [93.1%; 98.5%] and 98.7% [96.1%; 99.7%]; and F1-scores were 0.918, 0.895 and 0.863, respectively. Mean time per case and rater required for evaluation was 2.8 ± 0.9 s for Colored_outline, 1.7 ± 0.1 s for Colored_MIP, and 2.0 ± 0.4 s for Gray_outline. Conclusions: The 2D segmentation mask projection images enabled the detection of segmentation errors of automated 3D segmentations of left and right lungs based on MRI with high diagnostic accuracy, especially when using color-encoding. The method enabled evaluation within a matter of seconds per case. Segmentation mask projection images may assist in visual quality control of automated segmentations in large-scale studies.

Tomography doi: 10.3390/tomography11120134

Authors: Marcel Opitz Matthias Welsner Halil I. Tazeoglu Florian Stehling Sivagurunathan Sutharsan Dirk Westhölter Erik Büscher Christian Taube Nika Guberina Denise Bos Marcel Drews Daniel Rosok Sebastian Zensen Johannes Haubold Lale Umutlu Michael Forsting Marko Frings

Objective: Chest computed tomography (CT) is a key component of the diagnostic assessment of people with cystic fibrosis (PwCF) and is increasingly replacing chest radiography. Due to improvements in life expectancy, radiation exposure has become a growing concern in PwCF. Photon-counting CT (PCCT) has the potential to reduce the risk of radiation-induced malignancies while maintaining diagnostic accuracy. This study aimed to compare the radiation dose and image quality of low-dose high-resolution (LD-HR) and ultra-low-dose high-resolution (ULD-HR) CT protocols using PCCT in PwCF. Methods: This retrospective study included 72 PwCF, with 36 undergoing a LD-HR chest CT protocol and 36 receiving an ULD-HR protocol on a PCCT. The radiation dose and image quality were assessed by comparing the effective dose and signal-to-noise ratio (SNR). Three blinded radiologists evaluated the overall image quality, sharpness, noise, and assessability of the bronchi, bronchial wall thickening, and bronchiolitis using a five-point Likert scale. Results: The ULD-HR PCCT protocol reduced radiation exposure by approximately 65% compared with the LD-HR PCCT protocol (median effective dose: 0.19 vs. 0.55 mSv, p < 0.001). While LD-HR images were consistently rated higher than ULD-HR images (p < 0.001), both protocols maintained diagnostic significance (median image quality rating of “4-good”). The average SNR of the lung parenchyma was significantly lower with ULD-HR PCCT compared to LD-HR PCCT (p < 0.001). Conclusions: ULD-HR PCCT significantly reduced radiation exposure while maintaining good diagnostic image quality in PwCF. The effective dose of ULD-HR PCCT is only twice that of a two-plane chest X-ray, making it a viable low-radiation alternative for routine imaging in PwCF.

Tomography doi: 10.3390/tomography11120133

Authors: Salahaden R. Sultan Faisal Albin Hajji Abdulrahman Alhazmi Shahad Alamri Abrar Alsulami Ahmed Albukhari Asseel Filimban Bander Almutairi Ahmad Albngali Reham Kaifi Mohammad Khayat Mohammed Alkharaiji Mohammad Khalil Abrar Alfatni

Background: Ultrasound is the primary imaging modality for evaluating thyroid nodules, with echogenicity and nodule size serving as parameters for malignancy risk stratification. Though the TI-RADS classification system is standardized, interpretation varies among observers due to subjectivity, and can affect diagnostic consistency. This study aimed to evaluate the diagnostic and interobserver agreement of quantitative ultrasound gray-scale analysis and nodule area in differentiating benign from malignant solid thyroid nodules. Methods: This retrospective study reviewed 600 patients who underwent thyroid ultrasound at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, in 2023 and 2024. Of these 600, 107 adult patients with 116 solid thyroid nodules (96 benign and 20 malignant) who subsequently underwent ultrasound-guided fine-needle aspiration were included in the final analysis. From B-mode ultrasound images, the grayscale median (GSM) values of each nodule and adjacent normal thyroid tissue were measured using Adobe Photoshop. The GSM ratio (GSMr) was calculated by dividing nodule GSM by normal tissue GSM. Nodule size, taken as cross-sectional area, was assessed using ImageJ software version 1.53. The Mann–Whitney U test was used to compare GSMr and the area between benign and malignant nodules. Inter-observer agreement was evaluated using the intraclass correlation coefficient (ICC). Results: Malignant nodules had significantly lower GSMr compared to benign nodules (malignant: median 0.76, IQR 0.27; benign: median 0.88, IQR 0.55, p = 0.02). Malignant nodules were also significantly larger than benign nodules (malignant: median 2.77 cm2, IQR: 5.08; benign: median 1.78 cm2, IQR 1.65, p = 0.02). Inter-observer reproducibility was excellent for both GSMr (ICC = 0.998) and area (ICC = 0.997). Conclusions: Quantitative ultrasound assessment of grayscale echogenicity and nodule area provides valuable diagnostic information for differentiating benign from malignant solid thyroid nodules. These objective measures may enhance diagnostic confidence and support more precise clinical decision-making in thyroid nodule evaluation.

Tomography doi: 10.3390/tomography11120132

Authors: Maximilian Hinsen Armin Michael Nagel Nadine Bayerl Hans-Peter Fautz Thomas Benkert Matthias Stefan May Michael Uder Rafael Heiss

Lung nodules are a common radiological finding that can be caused by a variety of reasons, ranging from benign granulomas and scarring to the early stages of primary lung malignancies and metastases [...]

Tomography doi: 10.3390/tomography11120131

Authors: Wen Li Yuncheng Zhong Youfang Lai Lingshu Yin Daniel Sforza Devin Miles Heng Li Xun Jia

Objectives: Ultra-high dose-rate FLASH radiotherapy has demonstrated strong potential in reducing normal tissue toxicity while maintaining effective tumor control. However, its underlying radiobiological mechanisms remain unclear, highlighting the need for novel approaches to probe the effects of radiation during and immediately after delivery. This study presents the first exploration of 3D PET imaging of positron-emitting nuclei (PENs) generated by a FLASH proton beam. Methods: A home-built 12-panel preclinical small-animal PET system was employed for recording coincidence events. A 142.4 MeV FLASH proton beam with a 100 ms delivery time was directed into a solid water phantom. PET coincidence signals were recorded during the first 1 s and up to 11 min. The system’s capability for 3D localization was also assessed, and Monte Carlo simulations were performed for validation. Results: The PET system successfully recorded coincidence data within the first second, including the 100 ms beam delivery interval. Detector dead-time effects under the high beam flux were observed, leading to underestimated event counts. Following irradiation, the measured activity and decay behavior were consistent with simulations. The PET system accurately reconstructed the spatial distribution of PEN activities, with discrepancies in measured versus calculated line profiles ranging from 3.35–6.85%. Reconstructed PET images enabled reliable 3D localization with sub-millimeter accuracy in both lateral and depth dimensions. Conclusions: Our findings demonstrate that a multi-detector PET system is a promising tool for investigating the radiation effects of FLASH beams.

Tomography doi: 10.3390/tomography11110130

Authors: Longgao Liu Chufeng Zeng Lizhi Liu Shumin Zhou Weihua Wu Peng Lin Jianhua Fu Tiehua Rong Xu Zhang Xiaodong Su

Background: This research focused on evaluating the utility of multimodal radiomics integrated with machine learning to predict pathological complete response (pCR) in a prospective cohort of esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunochemotherapy (nICT). Methods: We retrospectively analyzed prospectively collected trial data from 66 ESCC patients. Radiomic features were extracted from computed tomography (CT) and magnetic resonance imaging (MRI) images. Four machine learning algorithms—Random Forest (RF), logistic regression, Support Vector Machine, and Extreme Gradient Boosting (XGBoost)—were applied with leave-one-out cross-validation to predict pCR after nICT. The predictive performance of the models was evaluated using receiver operating characteristic curve analysis. Results: In total, 851 features were identified. Among the four machine learning algorithms, the XGBoost machine learning method demonstrated the best model performance across CT, MRI, and clinical feature-based models. Furthermore, the integrated model demonstrated superior performance compared to individual models based solely on CT, MRI, or clinical features across all machine learning algorithms. Among these, the XGboost-based integrated model achieved the highest performance on the test set, with an AUC of 0.961, a TPR of 84.2%, a TNR of 95.7%, a PPV 88.9% of and a NPV of 93.8%. Decision curve analysis validated the model’s robust clinical utility, with calibration curves demonstrating strong concordance between predicted and observed therapeutic responses. Conclusions: The study demonstrates the potential for predicting pCR in patients with ESCC treated with standardized neoadjuvant chemotherapy and PD-1 inhibitors using machine learning methods that integrate multimodal CT and MRI images with clinical features.

Tomography doi: 10.3390/tomography11110129

Authors: Sangar Abdullah Güney Özkaya Adnan Gündoğdu Murat Şendur

Background: Preoperative evaluation in bariatric surgery aims to minimize perioperative risks and identify comorbid abdominal pathologies that may influence surgical planning. The role of routine abdominal ultrasonography (USG) remains debatable. Methods: This retrospective study included 1119 consecutive candidates for bariatric surgery who underwent routine preoperative ultrasonography (USG) between January 2022 and October 2024. Patients were stratified by BMI and categorized according to USG findings as normal, incidental, requiring follow-up/concomitant procedures, or necessitating cancellation. Baseline characteristics, USG findings, surgical outcomes, and predictors of cancellation were analyzed using univariate, multivariate, and Firth’s penalized logistic regression analyses. Ultrasonographic findings were further stratified as clinically significant (requiring intervention) or non-clinically significant (not requiring intervention) to standardize interpretation. Results: Abnormal USG findings were present in 77.5% of patients, with hepatic steatosis (60.8% [n = 680]), hepatomegaly (21.5%), and gallstones (13.9%) being the most frequent. Higher BMI was significantly associated with hepatomegaly, steatosis, and gallstones (all p < 0.05), but not with surgical cancellation. Bariatric surgery was cancelled in 11 patients (1.0%) due to critical findings exclusively identified on USG, including large ovarian/uterine masses, choledochal cysts, and suspected malignancies. In multivariate and Firth-adjusted regression, large ovarian/uterine masses (adjusted OR 12.9, 95% CI 3.0–55.2, p = 0.001; Firth OR 11.4, 95% CI 2.5–51.4, p = 0.002) and choledochal cysts (Firth OR 29.7, 95% CI 1.8–489.5, p = 0.048) emerged as independent predictors of cancellation. Conclusions: Although the overall cancellation rate was low, the detection of critical USG findings in 1.0% of patients had major clinical implications, preventing inappropriate or unsafe surgery and enabling timely referral for specialist management. Routine preoperative ultrasonography thus offers a clinically meaningful safeguard in bariatric surgery, supporting its inclusion in preoperative assessment algorithms.

Tomography doi: 10.3390/tomography11110128

Authors: Roch Listz Maurice

Rationale and Objective: Medical imaging, particularly computed tomography (CT), is the largest man-made contributor to collective radiation exposure. This study compares methods for assessing CT radiation dose, focusing on thoracic examinations. Population investigated: We retrospectively analyzed 3956 non-contrast thoracic CT exams from 1553 females (mean age 70 ± 12 years) and 2403 males (mean age 69 ± 12 years). Methods: Data were acquired using a Siemens Somatom Force CT-Scanner (installed in 2015). Exposure parameters and patient somatic data were recorded and used as inputs for the Virtual Dose Simulator (VDS), which served as the gold standard for effective dose (EDref) measurement. Additionally, ED was calculated using two ICRP-103 K-factor methods: Shrimpton et al. (EDshr) and Romanyukha et al. (EDrom). Results: Regression analysis demonstrated strong linear relationships between EDref and both weight and BMI (R2 ≥ 0.84), with EDref values ranging from 1.55 to 4.59 mSv. Even stronger linear relationships were observed between EDref and CT scanner tube current, particularly for women (R2 = 0.93) and men (R2 = 0.90). Similar trends emerged for dose-length product (DLP), which showed high correlations for both women (R2 = 0.95) and men (R2 = 0.94). Compared to VDS, EDrom underestimated women’s doses by 10% and slightly overestimated men’s doses by 1%, while EDshr underestimated the effective dose by 18% for women and 9% for men. Conclusion: This study demonstrates that K-factor methods provide a simple, efficient, and clinically practical approach for both individual cumulative dose monitoring (critical for patients requiring repeated imaging) and population-level dose assessment (essential for epidemiological risk evaluation). The high reliability of K-factor-based estimates, as demonstrated in this work, underscores their potential for integration into clinical practice to enhance dose optimization and patient safety.

Tomography doi: 10.3390/tomography11110126

Authors: Antonio Galluzzo Ginevra Danti Linda Calistri Diletta Cozzi Daniele Lavacchi Daniele Rossini Lorenzo Antonuzzo Sebastiano Paolucci Francesca Castiglione Luca Messerini Fabio Cianchi Vittorio Miele

Objectives: To develop two different radiomic models based on preoperative contrast-enhanced computed tomography (PP CT) to predict microsatellite instability (MSI) in patients with colorectal cancer (CRC) before surgery. Methods: PP CT scans of 115 CC patients were segmented using 3DSlicer (v5.6.1). Model I included images from three different scanners (GE, Siemens, Philips), while Model II used only one scanner (GE). For Model I, 80 patients were used for training and 35 for internal validation; for Model II, 46 and 24 patients were used, respectively. Data on sex, age, tumour location, and MSI genomic status were collected. A total of 107 radiomic features (RFs) were extracted, and 30 and 35 RFs were identified as relevant for Models I and II, respectively, using the t-test or Mann–Whitney test (p < 0.05). The most robust RFs were selected using the LASSO regression method. Both models were internally validated. Results: Model I, based on 2 RFs and 1 clinical feature (LOCATION) achieved an AUC of 0.76 (95% CI: 0.65–0.87) in the training cohort and 0.74 (95% CI: 0.56–0.92) in the validation cohort. Model II, based on 3 RFs, achieved an AUC of 0.85 (95% CI: 0.73–0.96) in the training cohort and 0.72 (95% CI: 0.50–0.94) in the validation cohort. Conclusions: Both radiomic models showed good performance in distinguishing between MSI and non-MSI tumours, potentially reducing the need for invasive histological testing and improving treatment timing. Despite achieving a higher AUC, Model II showed signs of overfitting when compared to Model I, which incorporated two RFs and one clinical feature (LOCATION). Radiomics may function as a non-invasive preoperative screening tool to inform decisions regarding MSI testing and treatment. Building radiomic models on larger, more diverse datasets is preferable to enhance generalizability and reduce overfitting.