Search Results (793)

Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors. Full article

The textile industry generates significant volumes of waste, making the development of reliable methods to evaluate biodegradability a pressing need. While standardised protocols exist for plastics, no specific methodologies have been established for textiles, and the quantification of non-degraded residues is commonly based on mass loss: a measurement that is prone to recovery errors. This study investigated the biodegradation of cotton, polyester, and cotton/polyester blend fabrics in soil under thermophilic conditions using a combined methodological approach. Carbon mineralisation was quantified through a respirometric assay that was specifically adapted for textile substrates, while residual solid fractions were assessed in situ by X-ray microtomography (micro-CT), thus avoiding artefacts associated with sample recovery. Complementary analyses were performed using SEM and FTIR to characterise morphological and chemical changes. Results showed substantial biodegradation of cotton, negligible degradation of polyester, and intermediate behaviour for the cotton/polyester blend. Micro-CT enabled the visualisation of fibre fragmentation and the quantification of the residual. The integration of respirometric, imaging, and spectroscopic techniques provided a comprehensive assessment of textile biodegradability. This study highlights the potential of micro-CT as a non-destructive tool to improve the accuracy and robustness of textile biodegradability assessment by enabling direct quantification of the residual solid fraction that can support future LCA studies and the development of standardised protocols for textile biodegradability. Full article

Background: Hip joint disorders exhibit diverse and overlapping radiological features, complicating early diagnosis and limiting the diagnostic value of single-modality imaging. Isolated imaging or clinical data may therefore inadequately represent disease-specific pathological characteristics. Methods: This retrospective study included 605 hip joints from Center A (2018–2024), comprising normal hips, osteoarthritis, osteonecrosis of the femoral head (ONFH), and femoroacetabular impingement (FAI). An independent cohort of 24 hips from Center B (2024–2025) was used for external validation. A multimodal deep learning framework was developed to jointly analyze radiographs, CT volumes, and clinical texts. Features were extracted using ResNet50, 3D-ResNet50, and a pretrained BERT model, followed by attention-based fusion for four-class classification. Results: The combined Clinical+X-ray+CT model achieved an AUC of 0.949 on the internal test set, outperforming all single-modality models. Improvements were consistently observed in accuracy, sensitivity, specificity, and decision curve analysis. Grad-CAM visualizations confirmed that the model attended to clinically relevant anatomical regions. Conclusions: Attention-based multimodal feature fusion substantially improves diagnostic performance for hip joint diseases, providing an interpretable and clinically applicable framework for early detection and precise classification in orthopedic imaging. Full article

Characterizing tight reservoirs is challenging due to the complex pore structure and strong heterogeneity at various scales. Current digital rock physics often struggles to reconcile high-resolution imaging with representative sample sizes, and 3D digital cores are frequently used primarily as visualization tools rather than predictive, computable platforms. Thus, a clear methodological gap persists: high-resolution models typically lack macroscopic geological features, while existing 3D digital models are seldom leveraged for quantitative, predictive analysis. This study, based on a full-diameter core sample of a single lithology (gray-black shale), aims to bridge this gap by developing an integrated workflow to construct a high-fidelity, computable 3D model that connects the micro–nano to the macroscopic scale. The core was scanned using high-resolution X-ray computed tomography (CT) at 0.4 μm resolution. The raw CT images were processed through a dedicated pipeline to mitigate artifacts and noise, followed by segmentation using Otsu’s algorithm and region-growing techniques in Avizo 9.0 to isolate minerals, pores, and the matrix. The segmented model was converted into an unstructured tetrahedral finite element mesh within ANSYS 2024 Workbench, with quality control (aspect ratio ≤ 3; skewness ≤ 0.4), enabling mechanical property assignment and simulation. The digital core model was rigorously validated against physical laboratory measurements, showing excellent agreement with relative errors below 5% for key properties, including porosity (4.52% vs. 4.615%), permeability (0.0186 mD vs. 0.0192 mD), and elastic modulus (38.2 GPa vs. 39.5 GPa). Pore network analysis quantified the poor connectivity of the tight reservoir, revealing an average coordination number of 2.8 and a pore throat radius distribution of 0.05–0.32 μm. The presented workflow successfully creates a quantitatively validated “digital twin” of a full-diameter core. It provides a tangible solution to the scale-representativeness trade-off and transitions digital core analysis from a visualization tool to a computable platform for predicting key reservoir properties, such as permeability and elastic modulus, through numerical simulation, offering a robust technical means for the accurate evaluation of tight reservoirs. Full article

Aluminum foam is attracting attention as a multifunctional, ultra-lightweight material. To apply this aluminum foam to actual industrial materials, aluminum foam plates are required. In addition, it is expected that a multi-layer aluminum foam composed of dissimilar aluminum alloy foam layers can further enhance its functionality. In this study, we attempted to fabricate a three-layer aluminum foam composed of commercially pure aluminum AA1050 and Al-Mg-Si aluminum alloy AA6061 by heating and foaming a total of three pieces of AA1050 precursor and AA6061 precursor arranged alternately, followed by immediate roller joining. It was found that, by traversing a roller immediately after foaming the AA1050 and AA6061 precursors, the aluminum foam could be joined while forming it into a flat plate. In addition, X-ray CT images of the fabricated samples revealed that material flow induced by roller traversing ruptured the surface skin layer. Numerous pores were observed within the sample, indicating pores were maintained during the roller traversing and no significant differences in porosities were identified between AA1050 aluminum foam and AA6061 aluminum foam. Furthermore, from the four-point bending test and the observation of samples after bending test, although quantitative mechanical properties were not obtained due to the as-joined samples were used for the bending test, pores were observed at the fracture surfaces, confirming that roller joining achieved seamless joining. Full article

Agricultural materials frequently undergo fragmentation due to high-stress conditions during processing, storage, and transportation. Throughout these processes, the spatial arrangement and morphology of particles continuously evolve, rendering the breakage behaviour of particle groups particularly complex. Thus, an in-depth understanding of the fracture processes and breakage mechanisms within particle beds holds significant research value. This study systematically investigates the breakage behaviour of rice particle groups under confined compression through an integrated methodology combining experimental testing, X-ray CT imaging, and finite element modelling (FEM) based on the cohesive zone model (CZM). Results demonstrate that, at the granular assembly scale, external loads are transmitted through force chains and progressively attenuate. As compression proceeds, stress disseminates toward peripheral particle regions. At the individual particle level, particle breakage results from the intricate interaction between coordination number (CN) and localized contact stress, with tensile stress playing a predominant role in the fracture process. An increase in coordination number promotes a more uniform stress distribution and inhibits breakage, thereby exhibiting a “protective effect”. These findings provide valuable insights for the design and optimization of grain processing equipment, contributing to a deeper comprehension of particle breakage characteristics. Full article

A lens-coupled high-speed X-ray camera, “Hayaka”, was developed for quick imaging of X-ray absorption fine structure (XAFS) and time-resolved high-speed computed tomography (CT) using synchrotron radiation (SR). This camera is a lens-coupled type, composed of a scintillator, an imaging lens system, and a high-speed visible light sCMOS, capable of imaging with a minimum exposure time of 1 μs and a maximum frame rate of 5000 frames/s (fps). A feasibility study using white and monochromatic SR at the beamline BL07 of the SAGA Light Source showed that fine X-ray images with a spatial resolution of 77 μm can be captured with an exposure time of 10 μs. Furthermore, quick imaging XAFS, combined with high-speed energy scanning of a small Ge double crystal monochromator of the same beamline, enabled spectral image data to be acquired near the Cu K-edge in a minimum of 0.5 s. Additionally, an in coquendo 4DCT (time-resolved 3D observation of cooking processes) observation combined with a high-speed rotation table revealed the boiling process of Japanese somen noodles over 150 s with a time resolution of 0.5 s. Full article

Background/Objectives: Fragility fractures of the pelvis (FFPs) are a significant concern in the elderly population, often leading to severe morbidity and mortality. This study aims to evaluate the diagnostic challenges, clinical outcomes, and mortality rates associated with FFPs in patients referred to multiple hospitals. Methods: A total of 99 patients with suspected pelvic fragility fractures were enrolled between January 2023 and June 2025. Initial diagnoses were made using plain X-rays, with computed tomography (CT) utilized to assess posterior ring fractures. Data on demographics, fracture types according to the Fragility Fracture of the Pelvis (FFP) Classification, hemoglobin levels, and mortality rates were collected and analyzed. Results: The findings revealed that while plain X-rays identified only anterior pelvic ring fractures, CT scans detected posterior ring fractures in 60.6% of cases. Patients with Nakatani II and III pelvic ramus fractures exhibited the most significant decreases in hemoglobin levels. The overall mortality rate was found to be 13.13%, with the highest rates observed in FFP I (13.5%) and FFP II (11.9%) groups. Conclusions: The findings of this study underscore the importance of CT imaging in the diagnosis of FFPs and highlight the need for close monitoring of hemoglobin levels in affected patients. This study also emphasizes the increased mortality risk associated with more complex fracture types. Future research should focus on evaluating functional independence and treatment outcomes to guide clinical decision-making in managing fragility fractures of the pelvis. Full article

Early and accurate diagnosis of lung diseases is essential for effective treatment and patient management. Conventional diagnostic models trained on a single data type often miss important clinical information. This study explored a multimodal deep learning framework that integrates cough sounds, chest radiograph (X-rays), and computed tomography (CT) scans to enhance disease classification performance. Two fusion strategies, early and intermediate fusion, were implemented and evaluated against three single-modality baselines. The dataset was collected from different sources. Each dataset underwent preprocessing steps, including noise removal, grayscale conversion, image cropping, and class balancing, to ensure data quality. Convolutional neural network (CNN) and Extreme Inception (Xception) architectures were used for feature extraction and classification. The results show that multimodal learning achieves superior performance compared with single models. The intermediate fusion model achieved 98% accuracy, while the early fusion model reached 97%. In contrast, single CXR and CT models achieved 94%, and the cough sound model achieved 79%. These results confirm that multimodal integration, particularly intermediate fusion, offers a more reliable framework for automated lung disease diagnosis. Full article

Objectives: The purpose of this study was to evaluate the trends and changes in the time to medical imaging in the emergency department (ED) for patients with cardiopulmonary symptoms during the coronavirus disease 2019 (COVID-19) pandemic. Methods: The retrospective observational study was conducted from the clinical database of a tertiary academic teaching hospital. Patients with cardiopulmonary symptoms (chest pain, dyspnea, palpitation and syncope) who visited an adult ED between January 2018 and December 2021 were included. The primary outcome was the time to medical imaging, including chest X-ray (CXR), chest computed tomography (CT), and focused cardiac ultrasound (FOCUS). The primary exposure was the date of the ED visit during the COVID-19 pandemic (from 1 March 2020 to 31 December 2021). Results: Among the 28,213 patients, 17,260 (61.2%) were in the pre-COVID-19 group, and 10,953 (38.8%) were in the COVID-19 group. The time to medical imaging was delayed in the COVID-19 group compared with the pre-COVID-19 group: the time to FOCUS was 9 min, the time to CXR was 6 min, and the time to chest CT was 115 min. Conclusions: We found that the time to medical imaging for patients with cardiopulmonary symptoms who visited the ED was significantly delayed during the COVID-19 pandemic. Full article

The coronavirus is highly contagious, making rapid early diagnosis essential. Although deep learning-based diagnostic methods using CT or X-ray images have advanced significantly, they still face limitations in cost, processing time, and radiation exposure. In addition, for the possibility of real-time COVID-19 diagnosis, model lightweighting is required. This study proposes a lightweight deep learning model for COVID-19 diagnosis based on fluorescence images and demonstrates its applicability in embedded environments. To prevent data imbalance caused by noise and experimental variations, images were preprocessed using Gray Scale conversion, CLAHE, and Z-Score normalization to equalize brightness values. Among the tested architectures—VGG, ResNet, DenseNet, and EfficientNet—ResNet152 and VGG13 achieved the highest accuracies of 97.25% and 93.58%, respectively, and were selected for lightweighting. Layer-wise importance was calculated using an imprinting-based method, and less important layers were pruned. The pruned VGG13 maintained its accuracy while reducing model size by 18.9 MB and parameters by 4.2 M. ResNet152 (Prune 39) improved accuracy by 1% while reducing size by 161.5 MB and parameters by 40.22 M. The optimized model achieved 129.97 ms, corresponding to 7.69 frames per second (FPS) on an NPU(Furiosa AI Warboy), proving real-time COVID-19 diagnosis is feasible even on low-power edge devices. Full article

Background/Objectives: Small bowel obstruction (SBO) is a common surgical emergency associated with significant morbidity and mortality. This retrospective analysis aimed to identify key predictors for the need for surgery in SBO management and to develop a simple clinical risk score to support decision-making. Methods: This retrospective study included 285 patients treated for SBO at the University Hospital Erlangen from 2018 to 2022. Pretherapeutic clinical, laboratory, and imaging data, as well as treatment details and outcome parameters were assessed and analyzed using univariate and multivariate logistic regression to identify significant predictors for the need of surgery. A weighted point-based risk score was then derived from the final model, and its discriminative performance was evaluated using receiver operating characteristic (ROC) analysis. Results: Of the 285 patients, 234 (82.1%) underwent surgery and 51 (17.9%) were successfully managed conservatively. Multivariate analysis identified the following independent predictors for surgery: 0–1 previous abdominal operation (OR 4.7, p = 0.009), serum albumin ≤ 34 g/L (OR 4.5, p = 0.011), free intraperitoneal fluid on imaging (OR 3.6, p = 0.015), air–fluid levels on plain abdominal X-ray (OR 3.5, p = 0.024) and a transition point on CT (OR 11.4, p = 0.002). A weighted score (range 0–6 points) was constructed, assigning 1 point to each of the first four predictors and 2 points to the transition point. The score showed good discrimination for predicting the need for surgery (AUC 0.874). Using a cut-off of ≥3 points, sensitivity was 96.2% and specificity 64.7%. The observed proportion of patients requiring surgery increased from 21.4% in the low-risk group (0–2 points) to 88.6% in the intermediate-risk group (3–4 points) and 97.3% in the high-risk group (5–6 points). Conclusions: The proposed predictors and the weighted risk score may support bedside decision-making in SBO by distinguishing patients who require surgery from those eligible for conservative management, but they require prospective multicenter validation before routine clinical implementation. Full article

Background: Conventional absorption-based computed tomography has a limited ability to resolve lung microarchitectures that are critical for histological subtype discrimination. This study evaluated the potential of X-ray Dark-Field Imaging Computed Tomography (XDFI CT) using synchrotron radiation for non-destructive, three-dimensional visualization of human lung cancer microstructures. Methods: Surgically resected human lung cancer specimens (n = 4) were examined, including acinar-predominant adenocarcinoma (n = 1), adenocarcinoma after concurrent chemoradiation therapy (n = 1), keratinizing squamous cell carcinoma (n = 1), and metastatic hepatocellular carcinoma in the lung (n = 1). Image acquisition was performed at beamline BL-14B of the Photon Factory (Tsukuba, Japan), using a monochromatic 19.8 keV synchrotron X-ray beam and a crystal analyzer-based refraction-contrast optical system. Imaging findings were qualitatively correlated with corresponding histopathological sections. Results: Synchrotron radiation XDFI CT enabled clear visualization of normal distal lung microanatomy, including alveolar walls and associated vascular structures, which served as internal references adjacent to tumor regions. Distinct microstructural features—such as invasive growth patterns, fibrotic or keratinized stroma, necrosis, and treatment-related remodeling—were identifiable and varied according to histological subtype. Tumor–normal tissue transitional zones were consistently delineated in all specimens. Conclusions: Synchrotron radiation XDFI CT provides high-resolution, non-destructive volumetric imaging of lung cancer tissues and reveals subtype-associated microarchitectural features. This technique may complement conventional histopathology by enabling three-dimensional virtual histologic assessment of lung cancer specimens. Full article

Objective: This study aimed to investigate the relationship between computed tomography (CT)-derived Hounsfield Unit (HU) measurements and dual-energy X-ray absorptiometry (DXA) and to evaluate the feasibility of using contrast-enhanced abdominal CT as a complementary tool in the assessment of bone health in liver transplant recipients. Methods: This retrospective descriptive and analytical study included adult liver transplant recipients who underwent both contrast-enhanced abdominal CT and DXA within a three-month interval. HU measurements were obtained from sagittal and axial reformatted images at the lumbar spine (L1–L4) and femoral neck. All CT examinations were performed using a standardized venous-phase protocol. DXA-derived T-scores from the lumbar spine and femur served as the reference standard. Correlation analyses and receiver operating characteristic (ROC) curves were used to evaluate associations between HU values and BMD, as well as the diagnostic performance of HU in identifying low bone density. Results: A total of 259 recipients (mean age 55.7 ± 14.4 years; 62.9% male) were included. Based on lumbar spine DXA, 17.8% had normal BMD, 36.7% were osteopenic, and 45.5% were osteoporotic. CT-derived HU values at both the lumbar spine and femoral neck were significantly lower in patients with reduced BMD and showed a graded decline across worsening DXA categories. HU values demonstrated positive correlations with corresponding T-scores. Diagnostic performance for detecting osteoporosis was fair, with AUCs of 0.700 (sagittal), 0.698 (axial), and 0.751 (femoral). Conclusion: Contrast-enhanced abdominal CT provides useful ancillary information for opportunistic bone health assessment. CT-derived HU values offer a rapid and cost-effective complementary tool but should not replace DXA in the diagnostic evaluation of osteoporosis Full article

Ensuring the integrity of medical images is essential to securing clinical workflows, telemedicine platforms, and healthcare IoT environments. Existing watermarking and reversible data-hiding approaches often modify pixel intensities, reducing diagnostic fidelity, introducing embedding constraints, or causing instability under compression and format conversion. This work proposes a distortion-free, non-embedding authentication framework that leverages the inherent stability of the most significant bit (MSB) patterns in the Non-Region of Interest (NROI) to construct a secure and tamper-sensitive reference for the diagnostic Region of Interest (ROI). The ROI is partitioned into fixed blocks, each producing a 256-bit SHA-256 signature. Instead of embedding this signature, each hash bit is mapped to an NROI pixel whose MSB matches the corresponding bit value, and only the encrypted coordinates of these pixels are stored externally in a secure database. During verification, hashes are recomputed and compared bit-by-bit with the MSB sequence extracted from the referenced NROI coordinates, enabling precise block-level tamper localization without modifying the image. Extensive experiments conducted on MRI (OASIS), X-ray (ChestX-ray14), and CT (CT-ORG) datasets demonstrate the following: (i) perfect zero-distortion fidelity; (ii) stable and deterministic MSB-class mapping with abundant coordinate diversity; (iii) 100% detection of intentional ROI tampering with no false positives across the six clinically relevant manipulation types; and (iv) robustness to common benign Non-ROI operations. The results show that the proposed scheme offers a practical, secure, and computationally lightweight solution for medical image integrity verification in PACS systems, cloud-based archives, and healthcare IoT applications, while avoiding the limitations of embedding-based methods. Full article