Search Results (5,492)

Background: Mitral valve prolapse is a common valvular heart disorder, usually associated with a benign prognosis in the absence of significant mitral regurgitation. However, a subset of patients is at increased risk for complex ventricular arrhythmias and sudden cardiac death. Identifying these high-risk individuals remains a major clinical challenge. Case Summary: We present the case of a 71-year-old female patient with recurrent syncopal episodes, a strong family history of sudden cardiac death, and complex ventricular ectopy. Multimodality imaging revealed bileaflet mitral valve prolapse, severe mitral regurgitation, mitral annular disjunction, and the Pickelhaube sign, with no evidence of myocardial fibrosis on cardiac magnetic resonance imaging. The patient underwent minimally invasive mitral valve repair and received an implantable cardioverter-defibrillator for primary prevention of sudden cardiac death. Follow-up revealed significant reverse cardiac remodeling, marked reduction in arrhythmic burden, and restoration of mitral valve function. Family screening identified mitral annular disjunction in both of her daughters, who were asymptomatic and without arrhythmias. Discussion: Mitral annular disjunction has emerged as a potentially arrhythmogenic substrate, especially in patients with familial clustering, raising the possibility of a genetic predisposition. Risk stratification remains difficult, as no individual clinical, electrocardiographic, or imaging marker has demonstrated consistent predictive value. Surgical correction of mitral valve prolapse with associated mitral annular disjunction may lead to a reduction in arrhythmic risk and promote favorable structural remodeling. Conclusions: This case-based review emphasizes the importance of advanced imaging techniques in the identification and management of high-risk mitral valve prolapse phenotypes. Early surgical intervention and close arrhythmic surveillance may improve outcomes, although further research is necessary to define risk assessment tools and explore the genetic background of arrhythmogenic mitral valve disease. Full article

This study proposes a region of interest (ROI) setting method to improve the accuracy and efficiency of fluorescence detection in a compact real-time multiplex fluorescence PCR system. Conventional commercial real-time PCR systems are limited in point-of-care (POC) environments due to their high cost and complex optical structures. To address this issue, we developed a low-cost, compact system using an open-platform camera and a Fresnel lens. However, in such a simply structured system, variations between the wells of the polymerase chain reaction (PCR) plate may affect the accuracy of fluorescence detection. In this study, after capturing images with a CMOS camera, we propose two ROI image processing algorithms. The proposed algorithms reliably extract fluorescence signals and compare ROI deviations caused by variations between wells to determine whether physical correction is necessary. To validate the system, we performed comparative analysis of real-time DNA amplification images and fluorescence dye images collected over multiple periods. Based on evaluations using manual detection as a reference, it was confirmed that even a simple algorithm can achieve stable fluorescence detection while minimizing ROI distortion. This study presents an efficient method for enhancing the accuracy of quantitative fluorescence analysis in small PCR systems and is expected to contribute to improving the performance of point-of-care diagnostics, thereby increasing accessibility to on-site diagnostics in the future. Full article

Under night vision conditions, visible-spectrum images often fail to capture background details. Conventional visible and infrared fusion methods generally overlay thermal signatures without preserving latent features in low-visibility regions. This paper proposes a novel deep learning-based fusion algorithm to enhance visual perception in night driving scenarios. Firstly, a local adaptive enhancement algorithm corrects underexposed and overexposed regions in visible images, thereby preventing oversaturation during brightness adjustment. Secondly, ResNet152 extracts hierarchical feature maps from enhanced visible and infrared inputs. Max pooling and average pooling operations preserve critical features and distinct information across these feature maps. Finally, Linear Discriminant Analysis (LDA) reduces dimensionality and decorrelates features. We reconstruct the fused image by the weighted integration of the source images. The experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods in both objective metrics and subjective visual assessments. Full article

To directly address the important issue of MRI geometric distortions in stereotactic radiosurgery (SRS) planning, we performed a phantom study of sequence acquisition optimization. This study analyzed, in particular, the effects of clinically relevant gadolinium (Gd) concentration as filling solution for the phantom, as well as phase encoding reversal direction and flip angle on distortion. We created a rigid geometric grid phantom with 840 fiducial markers for distortion quantification on a 3T MRI scanner. To choose the optimal filling solution, an anthropomorphic RANDO phantom was employed, and 1 mmol/L gadolinium was chosen due to clinical relevance. An automated Python-based software (version 3.7.1) was developed for efficient detection and matching of phantom inserts between MRI and CT scans. A series of MRI acquisition parameter optimizations were systematically evaluated. The standard SRS protocol exhibited the highest average distortion of 1.301 mm. Notably, reversing the phase-encoding direction to anterior–posterior (AP) reduced the mean distortion to 0.725 mm, a 44.27% decrease, while the maximum distortion was reduced by 15.65%. The AP phase sequence maintained acquisition time, SAR, SNR, and CNR within acceptable limits. Additional distortion reduction was achieved by increasing the flip angle from 12° to 18°. In this work, we succeeded in significantly reducing the mean distortion observed in phantom images. As the gadolinium concentration used in the phantom is clinically similar to the gadolinium concentration observed in patients undergoing MRI scans with contrast agents, the achieved distortion reduction is prospectively reproducible in patients. Full article

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error introduced by stop-and-go assumption. To this end, this paper proposes an improved extended wavenumber domain imaging algorithm for ultra-high-resolution SAR to systematically address the imaging quality degradation caused by these challenges. In the proposed algorithm, the one-step motion compensation method is employed to compensate for the errors caused by orbital curvature through range-dependent envelope shift interpolation and phase function correction. Then, the interpolation based on modified Stolt mapping is performed, thereby facilitating effective separation of the range and azimuth focusing. Finally, the residual range cell migration correction is applied to eliminate range position errors, followed by azimuth compression to achieve high-precision focusing. Both simulation and spaceborne data experiments are performed to verify the effectiveness of the proposed algorithm. Full article

Background: The epidermal growth factor receptor (EGFR) is an important factor in the behavior of diffuse glioma, serving as a potential biomarker for tumor aggressiveness and a therapeutic target. Diffusion tensor imaging (DTI) provides insights into the microstructural integrity of brain tissues, allowing for detailed visualization of tumor-induced changes in white matter tracts. This imaging technique can complement molecular pathology by correlating imaging findings with molecular markers and genetic profiles, potentially enhancing the understanding of tumor behavior and aiding in the formulation of targeted therapeutic strategies. The present study aimed to investigate the molecular properties of diffuse glioma based on DTI sequences. Methods: A total of 27 patients with diffuse glioma (in accordance with the WHO 2021 classification) were investigated using preoperative DTI sequences. The study was conducted using the tractography software DSI Studio (Hou versions 2025.04.16). Following the preprocessing of the raw data, volumes of the arcuate fasciculus (AF), frontal aslant tract (FAT), inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF) were reconstructed, and fractional anisotropy (FA) was derived. Molecular pathological examination was conducted to assess the presence of EGFR amplifications. Results: The mean age of patients was 56 ± 13 years, with 33% females. EGFR amplification was observed in 8/27 (29.6%) of cases. Following correction for multiple comparisons, FA in the left AF (p = 0.025) and in the left FAT (p = 0.020) was found to be significantly lowered in EGFR amplified glioma. In the right language network, however, no statistically significant changes were observed. Conclusions: EGFR amplification may be associated with lower white matter integrity of left hemispheric language tracts, possibly impairing neurological function and impacting surgical outcomes. The underlying molecular and cellular mechanisms driving this association require further investigation. Full article

Background/Objectives: Several sequences for magnetization transfer contrast (MTC) imaging are available, from indices of MTC ranging from quantitative magnetization transfer (qMT) that yields the macromolecular fraction to simple ratios of signal intensities with and without a magnetization transfer (MT) pulse. Aging muscle undergoes changes including an increase in fibrosis and adipose accompanied by fiber atrophy and loss. The objective is to evaluate five MTC sequences to study age-related differences in muscle tissue composition. Methods: The lower leg (calf) of 15 young (8M/7F, 25.8 ± 3.7 years) and 9 senior subjects (5F/4M, 68.4 ± 3.3 years) was imaged with the following sequences: multi-offset qMT fit to the Ramani and Yarnykh models, single-offset qMT two-parameter fit to the Ramani model, a semi-quantitative MT_sat sequence, magnetization transfer ratio (MTR), and MTR-corrected (MTR_corr) for B1 inhomogeneities. T1 mapping was also performed. Statistical analysis was performed to identify significant age-related and regional (intermuscular) differences. Results: Significant age-related decreases (p < 0.001) in macromolecular fraction (from two-parameter fit), MT_sat, MTR, and MTR_corr were identified. A significant age-related increase in T1 (p < 0.001) was also identified. Pearson correlation coefficients between T1 and MTC indices were weak to moderate but significant. Conclusions: Age-related decreases in MTC may reflect that loss of myofibrillar proteins dominates the increase in collagen content with age. Further, the modest correlation of MTC indices with T1 indicates that all the age-related differences in MTC cannot be explained by an increase in inflammation. The MT_sat sequence was identified as the most clinically relevant in terms of acquisition speed, post-processing simplicity, and ability to identify age-related differences in macromolecular fractions. Full article

In recent decades, technological developments in archaeological geophysics have led to growing data volumes, so that an important bottleneck is now at the stage of data interpretation. The manual delineation and classification of anomalies are time-consuming, and different methods for (semi-)automatic image segmentation have been proposed, based on explicitly formulated rulesets or deep convolutional neural networks (DCNNs). So far, these have not been used widely in archaeological geophysics because of the complexity of the segmentation task (due to the low contrast between archaeological structures and background and the low predictability of the targets). Techniques based on shallow machine learning (e.g., random forests, RFs) have been explored very little in archaeological geophysics, although they are less case-specific than most rule-based methods, do not require large training sets as is the case for DCNNs, and can easily handle 3D data. In this paper, we show their potential for geophysical data analysis. For the classification on the pixel level, we use ilastik, an open-source segmentation tool developed in medical imaging. Algorithms for object classification, manual reclassification, post-processing, vectorisation, and georeferencing were brought together in a Jupyter Notebook, available on GitHub (version 7.3.2). To assess the accuracy of the RF classification applied to geophysical datasets, we compare it with manual interpretation. A quantitative evaluation using the mean intersection over union metric results in scores of ~60%, which only slightly increases after the manual correction of the RF classification results. Remarkably, a similar score results from the comparison between independent manual interpretations. This observation illustrates that quantitative metrics are not a panacea for evaluating machine-generated geophysical data interpretation in archaeology, which is characterised by a significant degree of uncertainty. It also raises the question of how the semantic segmentation of geophysical data (whether carried out manually or with the aid of machine learning) can best be evaluated. Full article

Introduction: Maxillary setback in orthognathic surgery has been extensively discussed regarding its effects on bone healing and facial soft tissue profile; however, its impact on upper airway volume remains unclear. Objective: We evaluate the influence of maxillary setback combined with counterclockwise (CCW) rotation of the occlusal plane on upper airway dimensions. Methods: A retrospective observational case series was conducted with eight patients diagnosed with Class II malocclusion who underwent orthognathic surgery involving maxillary setback and CCW mandibular rotation. All procedures were performed by the same surgeon. Preoperative (T1) and 6-month postoperative (T2) facial CT scans were analyzed using Dolphin Imaging software11.7 to measure airway volume (VOL), surface area (SA), and linear distances D1, D2 and D3. Statistical analysis was performed using the Wilcoxon test with a 5% significance level. Results: Significant skeletal changes were observed, including 10.2 mm of mandibular advancement, 5.2 mm of hyoid advancement, and 4.1° of CCW rotation. Although increases in airway volume and surface area were noted, they did not reach statistical significance (p = 0.327 and p = 0.050, respectively), but suggesting a favorable trend toward airway adaptation. Conclusions: Maxillary setback combined with CCW rotation appears to safely correct Class II skeletal deformities without compromising upper airway space. These preliminary findings highlight the technique’s potential for both functional and aesthetic outcomes, warranting further long-term studies. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

Grid structured visual data such as forms, tables, and game boards require models that pair pixel level perception with symbolic consistency under global constraints. Recent Pixel Language Models (PLMs) map images to token sequences with promising flexibility, yet we find they generalize poorly when observable evidence becomes sparse or corrupted. We present GridMNIST-Sudoku, a benchmark that renders large numbers of Sudoku instances with style diverse handwritten digits and provides parameterized stress tracks for two tasks: Completion (predict missing cells) and Correction (detect and repair incorrect cells) across difficulty levels ranging from 1 to 90 altered positions in a 9 × 9 grid. Attention diagnostics on PLMs trained with conventional one dimensional positional encodings reveal weak structure awareness outside the natural Sudoku sparsity band. Motivated by these findings, we propose a lightweight Row-Column-Box (RCB) positional prior that injects grid aligned coordinates and combine it with simple sparsity and corruption augmentations. Trained only on the natural distribution, the resulting model substantially improves out of distribution accuracy across wide sparsity and corruption ranges while maintaining strong in distribution performance. Full article

Gallstone disease affects approximately 10–20% of the global adult population, with early diagnosis being essential for effective treatment and management. While image-based machine learning (ML) models have shown high accuracy in gallstone detection, tabular data approaches remain less explored. In this study, we have proposed a Random Forest (RF) classifier optimized using the Sand Cat Swarm Optimization (SCSO) algorithm for gallstone prediction based on a tabular dataset. Our experiments have been conducted across four frameworks: only RF without cross-validation (CV), RF with CV, RF-SCSO without CV, and RF-SCSO with CV. Only RF without CV model has achieved 81.25%, 79.07%, 85%, and 73.91% accuracy, F-score, precision, and recall, respectively, using all 38 features, while the RF with CV has obtained a 10-fold cross-validation accuracy of 78.42% using the same feature set. With SCSO-based feature reduction, the RF-SCSO without and with CV models have delivered a comparable accuracy of 79.17% and 78.32%, respectively, using only 13 features, indicating effective dimensionality reduction. SHAP analysis has identified CRP, Vitamin D, and AAST as the most influential features, and DiCE has further illustrated the model’s behavior by highlighting corrective counterfactuals for misclassified instances. These findings demonstrate the potential of interpretable, feature-optimized ML models for gallstone diagnosis using structured clinical data. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

Due to the local similarities between different facial expressions and the subjective influences of annotators, large-scale facial expression datasets contain significant label noise. Recognition-based noisy labels are a key challenge in the field of deep facial expression recognition (FER). Based on this, this paper proposes a simple and effective attention-based fusion consistency network (AFCN), which suppresses the impact of uncertainty and prevents deep networks from overemphasising local features. Specifically, the AFCN comprises four modules: a sample certainty analysis module, a label correction module, an attention fusion module, and a fusion consistency learning module. Among these, the sample certainty analysis module is designed to calculate the certainty of each input facial expression image; the label correction module re-labels samples with low certainty based on the model’s prediction results; the attention fusion module identifies all possible key regions of facial expressions and fuses them; the fusion consistency learning module constrains the model to maintain consistency between the regions of interest for the actual labels of facial expressions and the fusion of all possible key regions of facial expressions. This guides the model to perceive and learn global facial expression features and prevents it from incorrectly classifying expressions based solely on local features associated with noisy labels. Experiments are conducted on multiple noisy datasets to validate the effectiveness of the proposed method. The experimental results illustrate that the proposed method outperforms current state-of-the-art methods, achieving a 3.03% accuracy improvement on the 30% noisy RAF-DB dataset in particular. Full article