Physical and Engineering Sciences in Medicine最新文献

{"title":"Investigation of the tissue equivalence of typical 3D-printing materials for application in internal dosimetry using monte carlo simulations.","authors":"Ayse Karadeniz-Yildirim, Handan Tanyildizi-Kokkulunk","doi":"10.1007/s13246-025-01532-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01532-2","url":null,"abstract":"<p><p>This study evaluates the dosimetric accuracy of PLA and ABS 3D-printed phantoms compared to real tissues using Monte Carlo simulations in radionuclide therapy.</p><p><strong>Materials and methods: </strong>A phantom representing average liver and lung volumes, with a 10 mm tumor mimic in the liver, was simulated for radioembolization using 1 mCi Tc-99 m and 1 mCi Y-90. The dose distribution (DD) was compared across PLA, ABS, and real organ densities.</p><p><strong>Results: </strong>For Tc-99 m, PLA showed a + 5.6% DD difference in the liver, and ABS showed - 35.3% and - 40.9% differences in the lungs. For Y-90, PLA had a + 1.7% DD difference in the liver, while ABS showed - 34.2% and - 34.9% differences in the lungs.</p><p><strong>Conclusion: </strong>In MC simulation, PLA is suitable for representing high-density tissues, while ABS is appropriate for simulating moderately low-density tissues.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626064","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":"Fully automated template matching method for ECG-free heartbeat detection in cardiomechanical signals of healthy and pathological subjects.","authors":"Salvatore Parlato, Jessica Centracchio, Daniele Esposito, Paolo Bifulco, Emilio Andreozzi","doi":"10.1007/s13246-025-01531-3","DOIUrl":"https://doi.org/10.1007/s13246-025-01531-3","url":null,"abstract":"<p><p>Cardiomechanical monitoring techniques record cardiac vibrations on the chest via lightweight electrodeless sensors that allow long-term patient monitoring. Heartbeat detection in cardiomechanical signals is generally achieved by leveraging a simultaneous electrocardiography (ECG) signal to provide a reliable heartbeats localization, which however strongly limits long-term monitoring. A heartbeats localization method based on template matching has demonstrated very high performance in several cardiomechanical signals, with no need for a concurrent ECG recording. However, the reproducibility of that method was limited by the need for manual selection of a heartbeat template from the cardiomechanical signal by a skilled operator. To overcome that limitation, this study presents a fully automated version of the template matching method for ECG-free heartbeat detection, powered by a novel automatic template selection algorithm. The novel method was validated on 256 Seismocardiography (SCG), Gyrocardiography (GCG), and Forcecardiography (FCG) signals, from 150 healthy and pathological subjects. Comparison with all existing methods for ECG-free heartbeat detection was carried out. The method scored sensitivity and positive predictive value (PPV) of 97.8% and 98.6% for SCG, 96.3% and 94.5% for GCG, 99.2% and 99.3% for FCG, on healthy subjects, and of 85% and 95% for both SCG and GCG on pathological subjects. Statistical analyses on inter-beat intervals reported almost unit slopes (R² > 0.998) and limits of agreement within ± 6 ms for healthy subjects and ± 13 ms for pathological subjects. The proposed automated method surpasses all previous ECG-free approaches in heartbeat localization accuracy and was validated on the largest cohort of pathological subjects and the highest number of heartbeats. The method proposed in this study represents the current state of the art for ECG-free monitoring of cardiac activity via cardiomechanical signals, ensuring accurate, reproducible, operator-independent heartbeats localization. MATLAB^® code is released as an off-the-shelf tool to support a more widespread and practical use of cardiomechanical monitoring in both clinical and non-clinical settings.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626043","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 method in non-destructive testing for lead shielding exceeding 25 kg/m² using ¹⁸F.","authors":"Jeremy Berrell, Deborah Carrick, Jason Tse, Elaine Ryan","doi":"10.1007/s13246-025-01524-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01524-2","url":null,"abstract":"<p><p>Non-Destructive Testing (NDT) is a commonly used technique for barrier verification within radiation protection, ensuring compliance with national standards and state regulations. There are currently limited published methods in NDT for lead shielding above 25 kg/m², and thus this research aimed to develop a reproducible method to aid in 'in the field' NDT for lead barriers exceeding 25 kg/m² using a Fluorine-18 (¹⁸F) source. Due to the fast decay of ¹⁸F, the data generated within this research was compiled from Monte Carlo (MC) simulations using the PENELOPE engine, and the PENGEOM geometry system to model the proposed empirical setup. The model predicted the Transmission Factor (TF) through area densities (thickness) of lead attenuators up to 302.7 kg/m², with results validated by empirical measurements using a Source-to-Detector Distance (SDD) of 38.1 ± 0.05 cm and 52.7 ± 0.05 cm. However, the study was limited by the chosen activity of ¹⁸F at approximately 180 MBq, where the simulated TF curves demonstrated correspondence of data up to and including area densities of 162.4 kg/m² using a 38.1 ± 0.05 cm SDD, and 138.0 kg/m² with a 52.7 ± 0.05 cm SDD. Beyond these thicknesses, the empirical transmission curves deviated from simulated curves due to measurable transmissions becoming significantly reduced. This research demonstrated that using SDDs above 23 cm would provide sufficient near narrow beam conditions with the proposed experimental configuration for in-the-field NDT. The research aimed to develop an equation and method for NDT using a ¹⁸F source for lead barriers greater than 25 kg/m², with transmission data to be made available upon request to the author.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606332","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":"Book review: Life and Death Rays: Radioactive Poisoning and Radiation Exposure by Alan Perkins : CRC Press 2021, Boca Raton, USA, 230 pages, ISBN: 9780367456498.","authors":"Kwan Hoong Ng","doi":"10.1007/s13246-025-01528-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01528-y","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543937","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":"Virtual-simulation boosted neural network dose calculation engine for intensity-modulated radiation therapy.","authors":"Zirong Li, Yaoying Liu, Xuying Shang, Huashan Sheng, Chuanbin Xie, Wei Zhao, Gaolong Zhang, Qichao Zhou, Shouping Xu","doi":"10.1007/s13246-025-01523-3","DOIUrl":"https://doi.org/10.1007/s13246-025-01523-3","url":null,"abstract":"<p><p>The Monte Carlo (MC) dose calculation method is widely recognized as the gold standard for precision in dose calculation. However, MC calculations are computationally intensive and time-consuming. This study aims to develop a neural network-based dose calculation engine using a virtual simulation database, producing dose distributions with accuracy comparable to MC dose calculations. We established an unrestricted virtual simulation database employing specific rules and automated optimization techniques. Individual dose distributions for each beam were stored. A neural network was then constructed and trained using a 3D Dense-U-Net architecture. The model's accuracy was validated in intensity-modulated radiation therapy (IMRT) for nasopharyngeal carcinoma, cervical carcinoma, and lung cancer. A total of 31,967 single-beam doses were collected from 2,382 virtual plans. For clinical beam doses, the gamma passing rates under the 1 mm/1% and 2 mm/2% criteria improved significantly from 13.4 ± 4.8% and 37.5 ± 9.4% to 77.5 ± 7.7% and 95.6 ± 2.5%, respectively, using the model. The mean computation time was 0.017 ± 0.002 s. We successfully developed an automated training workflow for a neural network-based dose calculation model in fixed-beam IMRT. This workflow enables the generation of a substantial training dataset from a relatively small clinical dataset, resulting in a model that excels in accuracy and speed.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544023","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":"Feasibility of the 3D printer to design an intracavitary applicator for the treatment of cervical cancer patients with high dose rate brachytherapy system.","authors":"Ankur Mourya, Lalit Mohan Aggarwal, Sunil Choudhary, Neeraj Sharma, Ritusha Mishra, Chandra Prakash, Uday Pratap Shahi","doi":"10.1007/s13246-025-01529-x","DOIUrl":"https://doi.org/10.1007/s13246-025-01529-x","url":null,"abstract":"<p><p>Designing an intracavitary brachytherapy applicator with a 3D printer using Polyamide12 for Tandem-Ovoid configuration. Further, to evaluate its feasibility and initial clinical use for the treatment of cervical cancer patients with a High Dose Rate (HDR) brachytherapy system. SolidWorks, Computer Aided design software was used for the design of the intracavitary brachytherapy applicator. Hewlett-Packard Jet Fusion 4200 was used for printing different parts of applicators with Polyamide12 (PA12) material. Radiograph and CT images of printed material parts were taken in the air and water medium to see the visualization. Before use in the patient, necessary quality assurance tests were carried out by coupling it with a microSelectron HDR machine. X-ray markers were used to visualize the source path inside the uterine and vaginal tandems. Physical and clinical evaluations were performed with a prototype 3D-printed applicator to check its suitability for clinical use. Final Applicator design was created from multiple hit and trial methods in SolidWorks. Printed PA12 of ovoid parts having a mean Hounsfield unit (HU) value of - 75 HU. Quality tests on the PA12 intracavitary applicator performed with the microSelectron HDR brachytherapy machine were passed. The chances of uterine perforation were less due to the semi-rigidity of the PA12 applicator. The newly designed T-O-based applicator and dummy marker do not produce any artifacts on the CT images. A low-cost flexible plastic applicator was developed that allowed the user to guide the tandem into the uterus of a patient. The developed PA12 intracavitary brachytherapy applicator did not produce artifacts on CT images.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544019","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":"Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis.","authors":"Yu Shi Lau, Li Kuo Tan, Kok Han Chee, Chow Khuen Chan, Yih Miin Liew","doi":"10.1007/s13246-024-01509-7","DOIUrl":"10.1007/s13246-024-01509-7","url":null,"abstract":"<p><p>Neointimal coverage and stent apposition, as assessed from intravascular optical coherence tomography (IVOCT) images, are crucial for optimizing percutaneous coronary intervention (PCI). Existing state-of-the-art computer algorithms designed to automate this analysis often treat lumen and stent segmentations as separate target entities, applicable only to a single stent type and overlook automation of preselecting which pullback segments need segmentation, thus limit their practicality. This study aimed for an algorithm capable of intelligently handling the entire IVOCT pullback across different phases of PCI and clinical scenarios, including the presence and coexistence of metal and bioresorbable vascular scaffold (BVS), stent types. We propose a multi-task deep learning model, named TriVOCTNet, that automates image classification/selection, lumen segmentation and stent struts segmentation within a single network by integrating classification, regression and pixel-level segmentation models. This approach allowed a single-network, single-pass implementation with all tasks parallelized for speed and convenience. A joint loss function was specifically designed to optimize each task in situations where each task may or may not be present. Evaluation on 4,746 images achieved classification accuracies of 0.999, 0.997, and 0.998 for lumen, BVS, and metal stent features, respectively. The lumen segmentation performance showed a Euclidean distance error of 21.72 μm and Dice's coefficient of 0.985. For BVS struts segmentation, the Dice's coefficient was 0.896, and for metal stent struts segmentation, the precision was 0.895 and sensitivity was 0.868. TriVOCTNet highlights its clinical potential due to its fast and accurate results, and simplicity in handling all tasks and scenarios through a single system.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"251-271"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933187","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":"Evaluating the prognostic value of radiomics and clinical features in metastatic prostate cancer using [⁶⁸Ga]Ga-PSMA-11 PET/CT.","authors":"Kaylee Molin, Nathaniel Barry, Suki Gill, Ghulam Mubashar Hassan, Roslyn J Francis, Jeremy S L Ong, Martin A Ebert, Jake Kendrick","doi":"10.1007/s13246-024-01516-8","DOIUrl":"10.1007/s13246-024-01516-8","url":null,"abstract":"<p><p>Prostate cancer is a significant global health issue due to its high incidence and poor outcomes in metastatic disease. This study aims to develop models predicting overall survival for patients with metastatic biochemically recurrent prostate cancer, potentially helping to identify high-risk patients and enabling more tailored treatment options. A multi-centre cohort of 180 such patients underwent [⁶⁸Ga]Ga-PSMA-11 PET/CT scans, with lesions semi-automatically segmented and radiomic features extracted from lesions. The analysis included two phases: univariable and multivariable. Univariable analysis used Kaplan-Meier curves and Cox proportional hazards models to correlate individual features with overall survival. Multivariable analysis used the LASSO Cox proportional hazards method to create 13 models: radiomics-only, clinical-only, and various combinations of radiomic and clinical features. Each model included six features and was bootstrapped 1000 times to obtain concordance indices with 95% confidence intervals, followed by optimism correction. In the univariable analysis, 6 out of 8 clinical features and 68 out of 89 radiomic features were significantly correlated with overall survival, including age, disease stage, total lesional uptake and total lesional volume. The optimism-corrected concordance indices from the multivariable models were 0.722 (95% CI 0.653-0.784) for the clinical model, 0.681 (95% CI 0.616-0.745) for the radiomics model, and 0.704 (95% CI 0.648-0.768) for the combined model with three clinical and three radiomic features, when extracting radiomic features from the largest lesion only. While univariable analysis showed significant prognostic value for many radiomic features, their integration into multivariable models did not improve predictive accuracy beyond clinical features alone.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"329-341"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.","authors":"Branimir Rusanov, Martin A Ebert, Mahsheed Sabet, Pejman Rowshanfarzad, Nathaniel Barry, Jake Kendrick, Zaid Alkhatib, Suki Gill, Joshua Dass, Nicholas Bucknell, Jeremy Croker, Colin Tang, Rohen White, Sean Bydder, Mandy Taylor, Luke Slama, Godfrey Mukwada","doi":"10.1007/s13246-024-01513-x","DOIUrl":"10.1007/s13246-024-01513-x","url":null,"abstract":"<p><p>Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI. In-house experience and key bodies of work on ethics, standards, and best practices for AI in Radiation Oncology were reviewed to inform selection criteria and evaluation strategies. A retrospective analysis using the criteria was performed across six vendors, including a quantitative assessment using five metrics (Dice, Hausdorff Distance, Average Surface Distance, Surface Dice, Added Path Length) across 20 head and neck, 20 thoracic, and 19 male pelvis patients for AI models as of March 2023. A total of 47 selection criteria were identified across seven categories. A retrospective analysis showed that overall no vendor performed exceedingly well, with systematically poor performance in Data Security & Responsibility, Vendor Support Tools, and Transparency & Ethics. In terms of raw performance, vendors varied widely from excellent to poor. As new regulations come into force and the scope of AI auto-segmentation systems adapt to clinical needs, continued interest in ensuring safe, fair, and transparent AI will persist. The selection and evaluation framework provided herein aims to promote user confidence by exploring the breadth of clinically relevant factors to support informed decision-making.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"301-316"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11997002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EPSM 2024, Engineering and Physical Sciences in Medicine : 17-20 November 2024, Melbourne, Australia.","authors":"","doi":"10.1007/s13246-024-01511-z","DOIUrl":"10.1007/s13246-024-01511-z","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"421-523"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617659","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}