Search Results (5,087)

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${}^{\circ }$. 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. Full article

This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime remain critical challenges in advanced CT platforms, motivating the integration of distributed sensing and data-driven analytics. System logs spanning August 2024 to October 2025 were processed into 10-min intervals and converted into a structured dataset comprising 76 features derived from operational events, scanning parameters, and temporal dynamics. Two supervised learning models, the Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained to identify abnormal operating conditions. Both models delivered excellent classification performance, achieving an accuracy of 0.973. The SVM demonstrated balanced precision, recall, and F1-score metrics of 0.973, while the ANN outperformed in ranking and sensitivity to anomalies with an AUROC of 0.993 and an AUPRC of 0.976. This framework highlights the potential of sensor-driven machine learning in enabling early detection of system anomalies and optimizing maintenance planning within clinical CT environments. Full article

Bone resorption secondary to stress shielding is a leading cause of hip implant failure, primarily due to the stiffness mismatch between the femur and the prosthesis. Although anatomical stem designs generally provide improved load transfer, Dorr type C femurs often require straight stems to ensure adequate primary stability. This work presents a systematic approach to designing a straight, additively manufactured porous titanium hip stem aimed at minimizing stress shielding. The lattice architecture is customized to replicate the mechanical properties of bone based on patient-specific femoral CT scans. The performance of the resulting porous implant is numerically assessed under simplified physiological gait loading conditions. The implant behavior is evaluated through a homogenization strategy to model the lattice structure, significantly reducing the computational effort and making the methodology easily replicable. Compared to its full counterpart, the porous design achieves a significant reduction in predicted bone loss, suggesting that the proposed framework is a promising proof of concept for patient-specific implants. While further experimental validation and larger cohort studies are required, these findings highlight the potential of mechanically tunable porous structures to mitigate the stress shielding phenomenon in anatomical conditions such as Dorr type C femurs, which require straight stems. Full article

Background: Repeated brain computed tomography (CT) scans in children may result in substantial cumulative radiation exposure, particularly in young children, who are more sensitive to ionizing radiation. The purpose of the study was to assess the dose levels of radiation in patients who receive repeated brain CT during childhood and adherence rates to pediatric imaging protocols. Methods: A retrospective cross-sectional study was conducted among 177 patients aged ≤5 years who underwent two or more brain CT examinations with a total of 514 CT examinations. The information was gathered through the hospital Picture Archiving and Communication System (PACS), which included patient demographics, scan parameters, and scanner-reported dose indicators such as volume-averaged computed tomography dose index (CTDIvol) and dose-length product (DLP). The effective dose (ED) was calculated and compared with estimated doses based on a nominal pediatric CT protocol. Results: The findings indicated a great variation in scan parameters, with CTDIvol values of 8.9 to 51.7 mGy and DLP values of 177 to 1310 mGy.cm. The number of repeated scans showed a great increase in the cumulative ED (p < 0.001). The median doses in patients below the age of one year were greater than those in older children. There was also a closer relation of scanner-reported doses to adult protocols, which suggests a lack of an optimized pediatric setting. Conclusions: Children under 5 who undergo repeated brain CT scans may face excessive radiation exposure. The matter is aggravated by the fact that scans are performed repeatedly without optimization of the dose, which leads to significant cumulative ED. Full article

Background/Objectives: Lisfranc joint injuries are complex midfoot pathologies frequently associated with subtle radiologic findings and delayed diagnosis. Although ligamentous disruption is considered the primary mechanism, the contribution of intrinsic osseous morphology remains insufficiently investigated. Previous studies have primarily relied on two-dimensional measurements and limited morphometric parameters. Therefore, this study aimed to provide a comprehensive three-dimensional (3D) computed tomography (CT) based morphometric evaluation of the medial and central columns of the Lisfranc joint and to determine whether specific bony parameters are associated with injury predisposition. Methods: A total of 48 CT scans, including 23 from patients with Lisfranc joint injuries and 25 from healthy controls without midfoot trauma, were retrospectively analyzed. For both groups, 3D models of the first three metatarsals (M1–M3) and cuneiforms (C1–C3) were reconstructed to measure bone length, articular surface areas, volumes, M1–M2/M2–M3 depth differences, and dorsal step-off (dorsal subluxation of M2 relative to C2). Correlations of these measurements with M2 length were additionally assessed in each group. Results: Comparisons between injury and healthy control groups revealed no significant differences in bony morphometrics (p > 0.05). Correlation analysis showed that a longer M2 were associated with greater cuneiform volumes and larger metatarsal articular surface areas (p < 0.05). Conclusions: This comprehensive 3D morphometric assessment of the Lisfranc joint indicates that intrinsic bony anatomy alone is unlikely to represent a primary predisposing factor for Lisfranc injuries. The observed positive relationship between M2 length and cuneiform articular surface areas and volumes demonstrates structural interdependence within the medial and central columns. Overall, injury susceptibility does not appear to be explained by variations in osseous morphology alone. Full article

Fused deposition modeling (FDM)-based three-dimensional (3D) fabrication offers a viable approach to manufacturing highly customized carbon fiber-reinforced polyether ether ketone (CFR-PEEK) components with complex geometries. However, the mechanical properties and surface roughness of FDM-fabricated parts are strongly influenced by processing parameters, particularly layer thickness. This study investigates the influence of layer thickness (0.1 mm and 0.2 mm) on the surface roughness, crystallinity, mechanical properties, and morphological characteristics of FDM-printed 10% CFR-PEEK specimens. The specimens were characterized using mechanical testing, differential scanning calorimetry (DSC), confocal laser microscopy, X-ray micro-computed tomography (µCT), and scanning electron microscopy (SEM). The results show that specimens printed with a 0.2 mm layer thickness exhibit higher crystallinity and ball indentation hardness while also showing increased surface roughness and porosity, with µCT analysis revealing larger and more spatially clustered voids near the sub-perimeter regions. In contrast, specimens printed with a 0.1 mm layer thickness demonstrate higher tensile strength, elastic modulus, elongation at break, and compressive stress. SEM fractography further indicates improved interlayer bonding and a relatively cohesive fracture surface in specimens printed with a 0.1 mm layer thickness. These findings demonstrate clear layer-thickness-dependent processing–structure–property relationships in FDM-printed CFR-PEEK composites and provide guidance for optimizing printing parameters to achieve improved mechanical performance. Full article

Background/Objectives: To determine the optimal reconstruction parameters for accurate visualization of peripheral in-stent restenosis using photon-counting detector CT (PCD-CT), and to evaluate its potential advantages over energy-integrated detector CT (EID-CT). Methods: Endovascular peripheral stents with varying degrees of in-stent restenosis were scanned in a custom-made phantom using EID-CT (Somatom Force) and PCD-CT (Naeotom Alpha) under clinical acquisition protocols. EID-CT images were reconstructed with Bv40 and Bv59 kernels at 512 matrices. PCD-CT data were acquired in standard-resolution (SR) and ultra-high-resolution (UHR) modes. In both modes, images were reconstructed with multiple kernels (Bv40, Bv56 and Bv72) and matrix sizes (512 and 1024 matrix). In SR mode, additional virtual monoenergetic images (40–100 keV) were generated, while UHR mode included only polychromatic reconstructions. Quantitative image quality (noise, contrast, contrast-to-noise ratio [CNR]) was measured, and two blinded readers performed qualitative assessments of restenosis visualization. Results: PCD-CT with SR mode at VMI 40 keV achieved the highest image contrast and CNR, significantly outperforming EID-CT and PCD-CT_UHR under matched conditions (all p < 0.05). The sharper reconstruction kernel further enhanced the image contrast and improved subjective visualization despite increased image noise. Both readers ranked PCD-CT_{SR-Bv72-40keV} at 1024 matrix highest for detecting all degrees of restenosis, with excellent inter-reader agreement (ρ > 0.80). Conclusions: PCD-CT in SR mode at VMI 40 keV, specifically using the Bv72 kernel with a 1024 matrix, optimizes the visualization of peripheral in-stent restenosis. Compared to EID-CT, PCD-CT provides superior image quality and detectability of restenosis. Full article

Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards efficiency, characterized by the rise of linear-complexity architectures and the optimization of foundation models. This paper presents a comprehensive survey of efficient medical image segmentation methodologies, systematically reviewing the evolution from heavy, accuracy-driven models to lightweight, deployment-ready paradigms. In particular, we highlight the growing importance of efficient segmentation in multisensor medical imaging, where heterogeneous data sources such as CT, MRI, ultrasound, and infrared imaging introduce additional challenges in scalability and computational cost. We propose a novel taxonomy that categorizes these advancements into four distinct streams: (1) Mamba and State Space Models, which leverage selective scanning mechanisms to achieve global receptive fields with linear complexity; (2) Efficient Adaptation of Foundation Models, focusing on parameter-efficient fine-tuning and knowledge distillation to tailor the Segment Anything Model (SAM) for medical domains; (3) Advanced Lightweight Architectures, covering the resurgence of large-kernel CNNs and the emergence of Kolmogorov–Arnold Networks (KANs); and (4) Data-Efficient Strategies, including semi-supervised and federated learning to address annotation scarcity. Furthermore, we conduct a rigorous comparative analysis of representative algorithms on mainstream benchmarks, providing a granular evaluation of the trade-offs between segmentation accuracy and computational overhead. The survey also discusses key challenges in multisensor and multimodal settings, including modality heterogeneity, data fusion complexity, and resource constraints. Finally, we identify critical challenges and outline future research directions, serving as a roadmap for the development of next-generation efficient and scalable medical image analysis systems. Full article

Background/Objectives: The modified Budin radiographic technique is a practical alternative to CT for measuring femoral neck anteversion (FNA); however, the impact of hip abduction angle on its accuracy remains unclear. This experimental study examined how varying abduction angles affect agreement between modified Budin measurements and CT. Methods: Twenty-seven dry adult femora underwent CT scanning, and FNA was measured using a validated three-slice superimposition method as the reference standard. Modified Budin radiographs were obtained at 20°, 30°, and 40° of femoral abduction. Two orthopedic surgeons independently measured FNA on all images twice, with at least 15 days between measurements. Intra- and interobserver reliability were assessed using the intraclass correlation coefficient (ICC). Mean values per femur were analyzed. Agreement with CT was evaluated using Pearson correlation, Bland–Altman analysis, and absolute error comparisons across abduction angles. Results: Reliability was excellent across all modalities (ICC, 0.982–0.998). Mean CT-derived FNA was 10.0° ± 8.5°, compared with 9.1° ± 8.0° at 20°, 8.3° ± 7.8° at 30°, and 7.8° ± 7.5° at 40° of abduction (p < 0.001). Correlation with CT was strong at all positions, but systematic underestimation increased with abduction angle. Among the tested positions, 20° abduction showed the smallest bias, the narrowest limits of agreement, and the lowest absolute error. Conclusions: Hip abduction angle significantly influences the accuracy of the modified Budin view. Under controlled experimental conditions, 20° abduction provided the closest agreement with CT among the tested positions. These findings suggest that lower abduction angles may improve geometric accuracy, although clinical feasibility and performance must be confirmed in vivo before routine clinical application can be recommended. Full article

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. Full article

Background: Monitoring skeletal muscle mass (SMM) during radiotherapy (RT) is important, as SMM loss is associated with poorer clinical outcomes. Cone-beam CT (CBCT), acquired before each RT fraction, offers the potential to track the lumbar skeletal muscle index (LSMI) over time. However, CBCT has lower image quality than conventional CT. This study assessed the agreement between CT and CBCT and evaluated the reliability of LSMI measurements in patients with head and neck squamous cell carcinoma. Methods: Patients who underwent both CT and CBCT on the same day during RT were included. The cross-sectional muscle area at C3 was measured, converted to L3, and used to calculate the LSMI. Two researchers analyzed all scans, with one repeating the measurements. Agreement and reliability were quantified using intraclass correlation coefficients (ICCs) and visualized with Bland–Altman plots. Results: LSMI measurements showed excellent agreement between CBCT and CT (ICC: 0.97–0.99; 95% CI: 0.95–0.99). The intrarater (ICC: 0.99; 95% CI 0.98–0.99) and interrater reliability (ICC: 0.97; 95% CI: 0.66–0.99) were high. Bland–Altman plots, however, revealed wide limits of agreement. Conclusion: CBCT provides reliable LSMI measurements and agrees well with CT, but the observed variability suggests cautious interpretation. When both modalities are available, CT remains the preferred standard for SMM assessment. Full article

Lung nodule detection remains a critical challenge in clinical diagnostics due to the small size, weak contrast, and high background interference of nodules in CT scans. To address these issues, a novel deep neural network architecture, termed Daisy-Net, is proposed. This model incorporates dual attention mechanisms and inter-scale feature perception, consisting of two primary components: the Parallelized Patch and Spatial Context Aware (PPSCA) module and the Omni-domain Multistage Fusion (OMF) module. The PPSCA module enhances the extraction of fine-grained textures and boundary information through multi-branch patch perception and spatial attention. The OMF module employs omni-domain feature fusion and progressive stage-wise supervision to improve robustness and discrimination under complex conditions. The lung nodule detection task is formulated as a two-dimensional segmentation problem and evaluated on the LUNA16 dataset. In the post-binarization comparative evaluation, Daisy-Net achieves the best overall performance among all compared methods, with an Intersection over Union (IoU) of 81.41, a Dice coefficient of 89.75, a precision of 95.34, a sensitivity of 84.78, and a specificity of 99.9974. These findings indicate the model’s strong capability in detecting small pulmonary nodules accurately and reliably. Full article

Background/Objectives: Sarcopenia is a progressive skeletal muscle disorder linked to adverse outcomes. Computed Tomography (CT) can quantify skeletal muscle, while the Charlson Comorbidity Index (CCI) predicts mortality by categorising comorbidities. This study examined whether Computed Tomography of the Kidneys, Ureters, and Bladder (CT-KUB)-derived skeletal muscle measurements correlate with CCI scores in hospitalised patients. Methods: This retrospective study included all patients admitted with renal colic to the Urology Department, Blacktown Hospital and underwent cystoscopy between June 2022 and June 2025. Data were obtained from electronic medical records. CCI scores, incorporating age and comorbidities, generated 10-year survival estimates. CT-KUB scans were reviewed for psoas muscle perimeter, area, height, width and Hounsfield unit at the aortic bifurcation. Skeletal Muscle Index (SMI) was calculated as skeletal muscle area (SMA)/height². Associations between CCI, psoas muscle metrics and outcomes (length of stay, Intensive Care Unit (ICU) admission, Emergency Department (ED) re-presentation) were assessed using Pearson’s correlations and between-group comparisons. Results: A total of 397 patients were analysed. Median Length of Stay (LOS) was 1 day (mean = 1.92, SD = 1.88). ICU admission occurred in 2.3% of patients, and 18.6% re-presented to ED within 30 days. Both CCI survival percentage and psoas muscle metrics (including SMI) were significantly associated with LOS. Lower SMA, Hounsfield unit (HU), length and perimeter were linked to higher ICU admission risk. Neither CCI nor muscle measures predicted ED re-presentation. Conclusions: CCI and CT-derived muscle metrics were independently associated with outcomes such as LOS and ICU admission. Combining these measures may improve risk stratification, warranting further prospective evaluation. Full article

Deep shale of the Lower Cambrian Qiongzhusi Formation in the Sichuan Basin represents a critical frontier for shale gas exploration in China. However, systematic understanding of the multi-scale links among lamination type, mechanical anisotropy, and fracture complexity remains limited. Based on lamination characteristics and total organic carbon (TOC) content, core samples were classified into four types. Using a multi-scale approach (uniaxial compression, Brazilian splitting, in situ CT scanning, QEMSCAN, and SEM), this study elucidates how lamination structure controls mechanical anisotropy, failure modes, and fracture mechanisms. The novelties of this work are threefold: (1) quantitatively linking specific lamination types (ORM, OPM, PAFC, PAF) to anisotropic mechanical responses; (2) introducing 3D fractal dimensions to evaluate fracture network complexity; and (3) integrating micro- (SEM) and macro-scale tests to reveal the coupled control of weak planes and brittle minerals on fracture propagation. Results indicate that laminated shales exhibit pronounced mechanical anisotropy. Loading parallel to laminations induces tensile splitting along weak planes, significantly reducing strength. Conversely, perpendicular loading generates complex fracture networks of horizontal secondary fractures along laminae and vertical main fractures through the matrix. Furthermore, 3D fractal dimension analysis quantifies fracture network complexity as follows: organic-poor clay-feldspar laminated shale > organic-poor clay-feldspar-calcareous laminated shale > organic-rich massive shale. Microscopic observations confirm that fracture propagation is jointly governed by weak plane systems and brittle mineral content, which collectively determine macroscopic failure patterns. These findings clarify how lamination type controls the laboratory mechanical response and fracture morphology of deep shale and provide a laboratory-scale framework for comparing lamination-related differences in mechanical anisotropy and fracture complexity in the Qiongzhusi Formation. Full article

(1) Background: Creutzfeldt–Jakob disease (CJD) is a progressive neurodegenerative disorder, characterized by cognitive decline, and motor and psychiatric symptoms; it primarily affects the central nervous system; however, peripheral nervous system involvement has rarely been described, particularly as an atypical presentation. (2) Methods: A 78-year-old Caucasian man, a retired farmer with no family history of neurological disease, presented with diarrhea followed by progressive lower limb weakness, which eventually evolved into encephalopathy and generalized areflexia. An initial diagnosis of inflammatory neuropathy was considered; the diagnostic assessment included blood and cerebrospinal fluid testing, a CT whole body scan, brain MRI, neuropsychological testing, electroencephalography, a nerve conduction study and electromyography. (3) Results: Neurophysiological studies demonstrated an acute asymmetrical sensorimotor, predominantly axonal polyneuropathy, initially suggestive of an axonal form of inflammatory polyradiculoneuritis. This pattern was confirmed on follow-up neurophysiological assessment performed three weeks later. Unexpectedly, the diagnostic course ultimately led to a diagnosis of sporadic Creutzfeldt–Jakob disease, confirmed by post-mortem neuropathological examination. Based on these findings, we conducted a literature review to summarize the current evidence on CJD-related neuropathy. (4) Conclusions: Our case emphasizes the importance of maintaining clinical suspicion for CJD even in patients presenting with progressive lower limb weakness and suggests that peripheral neuropathy may be concomitant or even precede the CNS manifestations. Careful consideration is required to avoid misdiagnosis of inflammatory neuropathy in the context of neurodegenerative diseases such as CJD. Full article