Tomography, Volume 11, Issue 9 (September 2025) – 9 articles

Micro-computed tomography (micro-CT) is a commonly used tool for bone evaluation in animal model research. Micro-scale finite element analysis (µFEA) has been proposed to account for different loading scenarios, detailed three-dimensional (3D) bone structure, material properties, and distribution obtained from micro-CT to estimate bone mechanical properties and to predict its potential fracture. The in vivo application of µFEA has been limited to animal models due to the smaller bore size of micro-CT and the long scan time. This narrative review article describes studies that used micro-CT-based µFEA to predict bone mechanical competence, understand bone fracture and remodeling mechanisms, and to evaluate the impacts of the therapeutics, implants, and surgical interventions. Moreover, the concept, limitations, and future potentials of micro-CT-based FEA are discussed. Full article

Background: The COVID-19 pandemic has claimed thousands of lives worldwide. While infection rates have declined in recent years, emerging variants remain a deadly threat. Accurate diagnosis is critical to curbing transmission and improving treatment outcomes. However, the similarity of COVID-19 symptoms to those of the common cold and flu has spurred the development of automated diagnostic methods, particularly through lung computed-tomography (CT) scan analysis. Methodology: This paper proposes a novel deep learning-based approach for detecting diverse COVID-19 variants using advanced textural feature extraction. The framework employs a dual-channel convolutional neural network (CNN), where one channel processes texture-based features and the other analyzes spatial information. Unlike existing methods, our model dynamically learns textural patterns during training, eliminating reliance on predefined features. A modified local binary pattern (LBP) technique extracts texture data in matrix form, while the CNN’s adaptable internal architecture optimizes the balance between accuracy and computational efficiency. To enhance performance, hyperparameters are fine-tuned using the Adam optimizer and focal loss function. Results: The proposed method is evaluated on two benchmark datasets, COVID-349 and Italian COVID-Set, which include diverse COVID-19 variants. Conclusions: The results demonstrate its superior accuracy (94.63% and 95.47%, respectively), outperforming competing approaches in precision, recall, and overall diagnostic reliability. Full article

Background/Objectives: A thorough understanding of spinal anatomy is essential for diagnostic assessment and surgical intervention. Interspinous distance (ISD), neuroforaminal dimensions (NFDs), and disc space height (DSH) have each been studied separately; however, their interrelationship remains unstudied. Given the use of interspinous implants as a minimally invasive treatment for lumbar stenosis and degenerative disc disease, defining these relationships is of growing clinical significance. This study investigates the correlation between ISD and both NFDs and DSH in a normative population and whether ISD varies with demographic factors. Methods: A retrospective chart review was performed on 852 patients who underwent CT imaging of the lumbar spine. ISD was measured from L1 to L5 as the shortest distance between the most caudal tip of the superior spinous process and the inferior spinous process. DSH was measured at the anterior, middle, and posterior margins. NFDs were assessed in axial and sagittal views, including axial width, craniocaudal height, and foraminal area. Statistical analysis assessed correlations between ISD, NFDs, DSH, and demographic variables. Results: No strong correlation was observed between ISD and either NFDs or DSH. Slightly greater correlation was present at L1–L3, weakening at L4–L5, where interspinous implants are most commonly placed. Demographic analysis revealed no consistent relationship between ISD and ethnicity, sex, or BMI. While it may be expected that larger ISD correlates with greater NFDs or DSH, our findings do not support this assumption. Conclusions: ISD does not strongly correlate with NFDs or DSH, and demographic factors do not significantly influence ISD in a healthy population. Full article

Background: Thoracic kyphosis has been increasingly associated with altered intra-abdominal and diaphragmatic dynamics, potentially contributing to gastroesophageal reflux disease (GERD) and hiatal hernia (HH). While previous studies have shown a relationship between spinal deformities and GERD symptoms, these findings have been largely observational, with few morphometric analyses. No prior study has directly quantified the relationship between thoracic curvature and hiatal surface area (HSA) using standardized computed tomography (CT)-based methods. Furthermore, existing studies have typically focused on patients with visible hernias, limiting understanding of early, subclinical anatomical changes. This study addresses this gap by evaluating whether thoracic kyphosis is associated with measurable hiatal enlargement, even in the absence of overt HH. Methods: In this retrospective, single-center study, 100 adult patients (50 with thoracic kyphosis, defined as a Cobb angle of ≥50° and 50 age- and sex-matched controls) underwent multidetector CT (MDCT). Hiatal surface area (HSA) was measured on a standardized oblique axial plane aligned with the diaphragmatic crura. Correlation and multivariable regression analyses were performed to assess relationships between Cobb angle and HSA. Results: The kyphosis group showed significantly larger HSA than controls (5.14 ± 1.31 cm² vs. 3.59 ± 0.74 cm²; p < 0.001). A moderate positive correlation was found between Cobb angle and HSA (r = 0.336, p = 0.017). Multivariable analysis identified the Cobb angle as an independent predictor of HSA (β = 0.028; p = 0.017), while age and sex were not significant predictors. No overt herniation was present in any subject. Conclusions: This is the first CT-based morphometric study to demonstrate that thoracic kyphosis is associated with hiatal enlargement, even in the absence of overt herniation. These findings support the hypothesis that postural spinal deformities may predispose individuals to GERD by structurally remodeling the diaphragmatic hiatus. Full article

Objectives: Point-of-care ultrasound (POCUS) has become an integral part of emergency, intensive care, and perioperative medicine. However, the training and subsequent evaluation of POCUS users are still not standardized. The aim of the study was to develop and validate an assessment tool for POCUS users. Methods: After reviewing the existing literature and a multi-stage expert survey (Delphi method), consensus on twelve items for the assessment tool was reached. To validate the assessment tool, a group of volunteer doctors and medical students performed a POCUS examination using simple linear probe and more complex sector probe techniques. The examination was evaluated by two independent assessors using the created assessment tool. Then, four experts evaluated anonymized recordings of the examinations. We tested the reliability and validity, including internal consistency. Results: A total of 70 examinations were included. Of these, 19 examinations were carried out by physicians and 51 by medical students. A high inter-rater reliability (Cohen’s kappa 0.78 (linear weighted; SEM 0.37; p < 0.001) and Krippendorff’s alpha 0.895) was shown for the evaluation tool. To improve discriminative power and strengthen reliability, the assessment tool was modified using Cronbach’s alpha. Modification resulted in the removal of three items (patient positioning, ultrasound mode selection, and probe selection) from the tool. The mean values of instrument and expert ratings were now 2.62% apart (46.90% instrument vs. 44.29% expert). Pearson’s correlation coefficient between tool and expert ratings showed moderate to high validity (r = 0.69; p < 0.001). Conclusions: The new assessment tool is highly reliable and a valid tool for assessing POCUS skills. It holds strong potential for integration into medical education and training to objectify ultrasound skills. Further studies are required to investigate discriminatory power and transferability to other POCUS algorithms. Full article

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps. Full article

Objectives: Few studies have reported in vivo measurements of electron density (ED) and effective atomic number (Z_eff) in normal brain tissue. To address this gap, dual-energy computed tomography (DECT)-derived ED and Z_eff maps were used to characterize normal-appearing adult brain tissues, evaluate age-related changes, and investigate correlations with myelin partial volume (V_my) from synthetic magnetic resonance imaging (MRI). Materials and Methods: Thirty patients were retrospectively analyzed. The conventional computed tomography (CT) value (CT_conv), ED, Z_eff, and V_my were measured in the normal-appearing gray matter (GM) and white matter (WM) regions of interest. V_my and DECT-derived parameters were compared between WM and GM. Correlations between V_my and DECT parameters and between age and DECT parameters were analyzed. Results: V_my was significantly greater in WM than in GM, whereas CT_conv, ED, and Z_eff were significantly lower in WM than in GM (all p < 0.001). Z_eff exhibited a stronger negative correlation with V_my (ρ = −0.756) than CT_conv (ρ = −0.705) or ED (ρ = −0.491). ED exhibited weak to moderate negative correlations with age in nine of the 14 regions. In contrast, Z_eff exhibited weak to moderate positive correlations with age in nine of the 14 regions. CT_conv exhibited negligible to insignificant correlations with age: Conclusions: This study revealed distinct GM–WM differences in ED and Z_eff along with opposing age-related changes in these quantities. Therefore, myelin may have substantially contributed to the lower Z_eff observed in WM, which underlies the GM–WM contrast observed on non-contrast-enhanced CT. Full article

Objective: The objective of this study was to compare the performance and robustness of a deep learning reconstruction method against established alternatives for soft tissue CT image reconstruction. Materials and Methods: Images were generated from portal venous phase chest–abdomen–pelvis CT scans (n = 99) acquired on a dual-layer spectral detector CT using filtered back projection, iterative model reconstruction (IMR), and deep learning reconstruction (DLR) with three parameter settings, namely ‘standard’, ‘sharper’, and ‘smoother’. Experienced raters performed a quantitative assessment by considering attenuation stability and image noise levels in ten representative structures across all reconstruction methods, as well as a qualitative assessment using a four-point Likert scale (1 = poor, 2 = fair, 3 = good, 4 = excellent) for their overall perception of ‘smoother’ DLR and IMR images. One scan was excluded due to cachexia, which limited the quantitative measurements. Results: The inter-rater reliability for quantitative measurements ranged from moderate to excellent (r = 0.63–0.96). Attenuation values did not differ significantly between reconstruction methods except for DLR against IMR in the psoas muscle (mean + 3.0 HU, p < 0.001). Image noise levels differed significantly between reconstruction methods for all structures (all p < 0.001) and were lower than FBP with any DLR parameter setting. Image noise levels with ‘smoother’ DLR were predominantly lower than or equal to IMR, while they were higher with ‘standard’ DLR and ‘sharper’ DLR. The ‘smoother’ DLR images received a higher mean rating for overall image quality than the IMR images (3.7 vs. 2.3, p < 0.001). Conclusions: ‘Smoother’ DLR images were perceived by experienced readers as having improved quality compared to FBP and IMR while also exhibiting objectively lower or equivalent noise levels. Full article