Physical and Engineering Sciences in Medicine最新文献

{"title":"Investigation of the most suitable 3D printing method and tissue equivalent materials for mimicking glandular-adipose tissues for production of breast phantom.","authors":"Aziz Safi̇, Haluk Yücel","doi":"10.1007/s13246-026-01819-y","DOIUrl":"https://doi.org/10.1007/s13246-026-01819-y","url":null,"abstract":"<p><p>In this study, the usability of masked stereolithography (MSLA) and fused filament fabrication (FFF) type printers to produce breast phantom is investigated. ABS, PET, PLA+, PVA and TPU-95 A filaments are used on FFF type printer with infill ratios between 70 and 100%; ABS-like, PLA and PMMA-like resins are used on SLA type printer with infill ratio 100% to tissue equivalent materials. The materials' CT scans are taken at 80, 100 and 120 kVp and the Hounsfield Units (HU) dependency on the infill ratio for each filament and resin is investigated. The materials' physical densities are measured; electron densities and effective atomic numbers are calculated. The suitability of the materials for mimicking adipose, glandular and skin tissue investigated in terms of HU value, physical density, electron density and effective atomic number. It is found that MSLA type printer is not eligible for printing breast tissue equivalent materials as the common resins have higher physical density and it is not possible to lower physical density by infill ratio. However, FFF type printer technology is more eligible for mimicking breast tissue. While ABS is suitable only for mimicking adipose tissue, PET, PLA+, PVA and TPU-95 A filaments can be used to produce adipose, glandular and skin tissues by just changing infill ratio. For production of a breast phantom via FFF printing, PET and PVA among the tested filament materials are preferable over the properties of PLA + and TPU-95 A due to their proper hydrogen(H) and carbon contents (C), and other physical and radiological properties.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Yolo-c2ecasa: a lightweight attention-enhanced model for three-class brain tumor localization and classification in MRI.","authors":"Shuo Zhang, Mohd Rizal Arshad, Haidi Ibrahim, Junting Zou","doi":"10.1007/s13246-026-01816-1","DOIUrl":"https://doi.org/10.1007/s13246-026-01816-1","url":null,"abstract":"<p><p>Brain tumor localization and type classification in MRI are important tasks in computer-aided medical image analysis. However, achieving competitive localization and classification performance with lightweight models remains challenging because reducing model complexity may weaken feature representation and localization ability. In this paper, we propose YOLO-C2ECASA, a lightweight attention-enhanced object detection framework based on YOLOv11 for three-class tumor localization and classification in tumor-positive MRI images. The present study focuses on glioma, meningioma, and pituitary tumors and does not address tumor-versus-no-tumor screening. The proposed C2ECASA module replaces the original self-attention design with a parameter-efficient spatial-channel attention mechanism. It integrates improved channel attention, lightweight spatial attention, C2-based feature preservation, and adaptive fusion to enhance lesion-relevant feature representation with limited computational cost. The proposed model was compared with representative object detection models. In the single-run comparison with representative object detectors, YOLO-C2ECASA maintained competitive detection performance while requiring only 2.4M parameters, 6.2G FLOPs, a model size of 5.01 MB, and a GPU inference time of 1.9 ms per image. More importantly, across ten independent runs under scratch initialization, YOLO-C2ECASA achieved mean mAP@0.5 and mAP@0.5:0.95 values of [Formula: see text] and [Formula: see text]. Under pretrained initialization, the corresponding repeated-run results were [Formula: see text] and [Formula: see text], respectively. Under CPU-only deployment, YOLO-C2ECASA achieves an inference time of 27.4 ms per image, further demonstrating its feasibility for resource-constrained environments. These results indicate that the proposed model achieves competitive detection performance while maintaining a lower computational cost than the compared detectors. Ablation and repeated-run experiments further demonstrate that the complete C2ECASA configuration achieves the highest average performance among the evaluated configurations while maintaining low computational complexity. Overall, the proposed method provides a favorable accuracy-efficiency trade-off for lightweight brain tumor localization and type classification in tumor-positive MRI images.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on ensemble model assisted classification of gastrointestinal bleeding using wireless capsule endoscopy.","authors":"Anastasios Koulaouzidis","doi":"10.1007/s13246-026-01814-3","DOIUrl":"https://doi.org/10.1007/s13246-026-01814-3","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACPSEM guideline: commissioning and quality assurance of Magnetic Resonance Imaging Linear Accelerators.","authors":"Luis Munoz, Reza Alinaghi-Zadeh, Stephen Gibson, Urszula Jelen, Marcus Powers, Sandra Fisher, Nick Cook, Christopher Colyer, Zoë Moutrie","doi":"10.1007/s13246-026-01803-6","DOIUrl":"https://doi.org/10.1007/s13246-026-01803-6","url":null,"abstract":"<p><p>MRI-guided radiotherapy introduces unique quality assurance (QA) requirements due to magnetic field effects on dose delivery, imaging-dependent workflows, adaptive planning and system specific considerations. Consistent, vendor agnostic guidance is needed to support safe commissioning, clinical implementation, and ongoing performance monitoring across MRI-Linac platforms. This guideline defines minimum recommended MRI-Linac commissioning and QA practices to support consistent and safe clinical operation across services. It was developed by the MRI-Linac QA sub-group of the Magnetic Resonance Imaging Linac Working Group using an evidence-informed, consensus-based approach. Key international standards, task group reports, vendor documentation, and peer-reviewed literature relevant to the MRI-Linac were reviewed. Candidate QA tests were mapped to clinical risks and system functions, then organized into commissioning and routine QA domains with suggested cadence. Recommendations were refined through iterative working group review, with emphasis on implementation and auditability. The resulting commissioning recommendations are grouped into domains addressing key performance and safety areas for MRI-Linacs. Additionally, a minimum test set is proposed with suggested frequencies and tolerances where supported by evidence. Adoption of this guideline supports harmonized MRI-Linac QA practice, improved reproducibility across sites, and enhanced patient safety. The framework is designed to be implementable across platforms and evolve as MRI-guided radiotherapy workflows, vendor capabilities, and the evidence matures.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A conical-reflector scintillation device for 3-dimensional measurement of radiotherapy beams: a feasibility study.","authors":"Benjamin Broadhead, Scott Crowe, Tanya Kairn, Samuel Peet, Andrew Fielding, Timothy Moroney, Prabhakar Ramachandran, Jamie Trapp","doi":"10.1007/s13246-026-01811-6","DOIUrl":"https://doi.org/10.1007/s13246-026-01811-6","url":null,"abstract":"<p><p>This study presents a novel optical dosimetry system designed to support three-dimensional characterisation of radiotherapy beams using a single-camera imaging geometry. The system combines a large-volume cylindrical plastic scintillator with a conical reflector, enabling scintillation light emitted during beam irradiation to be captured as both direct emission and secondary reflection arcs using a single camera. These reflected signals encode spatial information about the beam's position, size, and intensity, forming the basis for extracting clinically relevant parameters such as beam geometry and intensity from a single 2D image. Controlled experiments using a 6 MV photon beam investigated the system's response to changes in beam position, lateral width, and penumbra shape. Analysis of reflection arc geometry and intensity demonstrated strong linear correlations between beam position and arc radius over lateral and longitudinal shifts of up to 60 mm and 70 mm respectively, and a clear non-linear relationship between the average arc intensity and lateral beam field widths between 10 and 80 mm. Additionally, penumbra widths derived from reflection arc profiles were linearly proportional to those measured directly in the scintillator, with the arc penumbra approximately [Formula: see text] times wider than the underlying scintillator observed beam penumbra. This proof-of-concept study demonstrated that reflection arc geometry and intensity in a single 2D image provided sufficient information to infer spatial beam position, lateral field width, and penumbra characteristics for static irradiations. The simplicity, symmetry, and static design of the system support future development towards volumetric beam reconstruction and, through sequential image acquisition, potentially time-resolved treatment verification.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical deep learning solutions for prostate cancer segmentation and implementation in radiotherapy planning.","authors":"Diyana Afrina Hizam, Marniza Saad, Ngie Min Ung, Asyraf Muaadz, Firdaus Mohd Salleh, Li Kuo Tan","doi":"10.1007/s13246-026-01815-2","DOIUrl":"https://doi.org/10.1007/s13246-026-01815-2","url":null,"abstract":"<p><p>Accurate segmentation of the prostate and surrounding structures is crucial in radiotherapy planning to ensure effective treatment delivery while minimising radiation exposure to nearby tissues. However, existing deep learning (DL) segmentation studies often exclude clinically important structures such as the penile bulb (PB) and seminal vesicles (SV), rely heavily on large proprietary datasets that are difficult to acquire in clinical settings, and lack direct comparisons with traditional atlas-based methods. To address these gaps, this study provides practical insights for hospitals considering the implementation of DL systems by evaluating 2D U-Net models across six structured experiments, including one experiment comparing a 3D U-Net model. Notably, this is the first study to utilises publicly available prostate CT data in DL training and integrates public and private datasets to improve model robustness for prostate and adjacent anatomical structures. The inclusion of underrepresented structures, such as PB and SV, enhances the clinical applicability of the models. Our results demonstrated that moderate-sized datasets, ranging from 60 to approximately 300 in our study, can achieve sub-millimetre Mean Distance Agreement (MDA) for key organs at risk like the bladder (0.60 mm) and rectum (0.93 mm), reducing reliance on large-scale annotated data. Models trained on routine clinical contours performed well without extensive manual refinement, demonstrating that existing clinical data can be leveraged for model training. In comparisons with four commercial atlas-based tools, the 2D U-Net models achieved superior accuracy for key anatomical structures, including the prostate, bladder, and SV, with our deep learning model achieving an MDA of 1.28 mm for the prostate compared to the 1.4-2.5 mm range of the atlas tools. Although 3D models provided improved spatial context for small structures such as SV, 2D models proved to be a practical alternative due to lower computational demands, making them suitable for resource-limited clinical settings.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monte Carlo investigation of anode-filter combinations on X-ray spectra in conventional radiology using PHITS.","authors":"Alex Eclador Ngankem, Joel Cebastien Guembou Shouop, Francis Chamberlain Djoumessi Zamo, Daniel Bongue, Maurice Moyo Ndontchueng","doi":"10.1007/s13246-026-01813-4","DOIUrl":"https://doi.org/10.1007/s13246-026-01813-4","url":null,"abstract":"<p><p>This research project investigates conventional radiology protocols by using PHITS (Particle and Heavy Ion Transport code System) Monte Carlo simulations to evaluate anode-filter configurations at tube voltages up to 150 kV. The study's innovation lies in its quantitative comparison of simulated spectra with SpekCalc, showing a mean deviation of less than 5%. The results indicate that Tungsten (W) and Gold (Au) anodes (K-lines: 59-68 keV) are optimal for radiography, whereas Molybdenum (Mo) and Rhodium (Rh) anodes (K-lines: 17-23 keV) are superior for mammography. By quantifying the impact of heavy filtration on beam hardening, this work establishes a robust framework for improving image contrast and supporting dose optimisation through appropriate selection of tube parameters.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Predicting VMAT patient-specific quality assurance outcome using a machine learning workflow.","authors":"Suvankar Das, A Robert Xavier, B Paul Ravindran","doi":"10.1007/s13246-026-01810-7","DOIUrl":"https://doi.org/10.1007/s13246-026-01810-7","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic and interventional radiology medical physics community in the Asia-Pacific region: an IAEA individual-level survey of medical physicists on professional perceptions and workforce challenges.","authors":"Ioannis Delakis, Zoe Brady, Anchali Krisanachinda, Md Nahid Hossain, Sann Chanthoubopha, Xile Zhang, Rajesh Kumar, Lukmanda Evan Lubis, Supriyanto Ardjo Pawiro, Indah Lestariningsih, Hideyuki Mizuno, Toshioh Fujibuchi, Siti Normasitah Masduki, Siti Norsyafiqah Mohd Mustafa, Thinn Thinn Myint, Kanchan P Adhikari, Darin O'Keeffe, Atif Masood, Luwis Gabriel, Ramon Carlo L Cruzpero, Vincent Dalin, Soma Somanesan, Joel Low, Ranaweera Koralalage Aruni Prabha, Banneheka Mudiyanselage Anuradha Tharanganee Banneheka, Van Nguyen Hung, Thi Hoang Hau, Olivera Ciraj-Bjelac","doi":"10.1007/s13246-026-01799-z","DOIUrl":"https://doi.org/10.1007/s13246-026-01799-z","url":null,"abstract":"<p><p>Diagnostic and interventional radiology services are expanding across the Asia-Pacific region, increasing the demand for clinically qualified medical physicists. Despite this growth, there is limited individual-level input on the challenges faced by the medical physics radiology community across the region, as previous regional and international workforce surveys have largely relied on data reported by national professional bodies or designated country representatives. This study reports the findings of an individual-level survey conducted under an International Atomic Energy Agency (IAEA) project on \"Improving the Quality and Safety of Diagnostic and Interventional Radiology Services to Benefit Health Care by Enhancing the Status, Knowledge and Skills of Medical Physicists (RAS6109)\". The aim of the survey was to capture professional perceptions and workforce challenges of medical physicists in diagnostic and interventional radiology. The survey was distributed online between November 2025 and January 2026 (three months) to medical physicists practising in, or with an interest in, diagnostic and interventional radiology. A total of 770 valid responses from the Asia-Pacific region were analysed. The findings showed that there is a substantial proportion of early-career professionals, frequent cross-multidisciplinary practice and heterogeneous training pathways with limited access to structured and supervised clinical training. Professional activities are predominantly focused on radiation protection and quality assurance, while respondents expressed a clear desire to increase their involvement in research and broader clinical activities. Key perceived barriers to workforce development include limited understanding of the medical physicist role, unclear role definition, and the absence of regulatory requirements. The findings from the survey highlight the need for strengthened clinical training pathways, clearer regulatory requirements aligned with international standards and guidelines, enhanced professional recognition, and sustained regional capacity-building initiatives to support the development of radiology medical physics in the Asia-Pacific region. Individual-level insights from this study complement existing national and institutional surveys and provide an evidence base to support workforce development and capacity-building initiatives in the region.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atherosclerosis diagnosis from magnetic resonance images: an empirical approach to automate carotid vessel wall segmentation and feature-based prediction.","authors":"Sriman Bidhan Baray, Rusab Sarmun, M Sajidul Islam, Md Shaheenur Islam Sumon, Adam Mushtak, M Murugappan, Haniza Yazid, Muhammad E H Chowdhury","doi":"10.1007/s13246-026-01809-0","DOIUrl":"https://doi.org/10.1007/s13246-026-01809-0","url":null,"abstract":"<p><p>Carotid artery atherosclerosis is a severe vascular condition linked to stroke and cardiovascular complications, requiring early and precise diagnosis. Manual segmentation of carotid artery structures in MRI is time-consuming and prone to interobserver variability. This study proposes and validates an automated framework combining deep learning (DL)-based segmentation with machine learning (ML) classification for diagnosing carotid atherosclerosis from low-resolution MRI VISTA images. We used 30 patient cases from the COSMOS 2022 MICCAI dataset, comprising 3238 annotated 2D axial slices from 3D scans. A subject-independent five-fold cross-validation strategy was employed, with 647 test images per fold. Data imbalance was addressed using augmentation and class balancing. All scans were acquired using a 3T Philips MRI system with a 3D VISTA black-blood sequence (TR = 800 ms, TE = 20 ms, resolution = 0.6 × 0.6 × 0.6 mm³). Expert-guided annotations were performed by trained researchers. A DenseNet121-UNet++-based model, enhanced through ablation-driven improvements, was used for segmentation, evaluated using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). The proposed method achieved a DSC of 85.76% and IoU of 73.68%. For classification, 466 handcrafted features were extracted and ranked using XGBoost, Random Forest, and Extra Trees, followed by stacking ensemble learning. The classification accuracy improved to 86.94%, outperforming individual models. The results demonstrate that improved segmentation significantly enhances classification performance. The proposed hybrid DL-ML pipeline provides an accurate, interpretable, and clinically feasible solution for automated carotid atherosclerosis diagnosis.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}