Physics in medicine and biology最新文献

{"title":"Deep learning-driven contactless ECG in MRI via beat pilot tone for motion-resolved image reconstruction and heart rate monitoring.","authors":"Haoyu Sun, Qichen Ding, Sijie Zhong, Zhiyong Zhang","doi":"10.1088/1361-6560/ae0c52","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0c52","url":null,"abstract":"<p><strong>Objective: </strong>Electrocardiogram (ECG) is crucial for synchronizing cardiovascular magnetic resonance imaging (CMRI) acquisition with the cardiac cycle and for continuous heart rate monitoring during prolonged scans. However, conventional electrode-based ECG systems in clinical MRI environments suffer from tedious setup, magnetohydrodynamic (MHD) waveform distortion, skin burn risks, and patient discomfort. This study proposes a contactless ECG measurement method in MRI to address these challenges.</p><p><strong>Approach: </strong>We integrated Beat Pilot Tone (BPT)-a contactless, high motion sensitivity, and easily integrable RF motion sensing modality-into CMRI to capture cardiac motion without direct patient contact. A deep neural network was trained to map the BPT-derived cardiac mechanical motion signals to corresponding ECG waveforms. The reconstructed ECG was evaluated against simultaneously acquired ground truth ECG through multiple metrics: Pearson correlation coefficient, relative root mean square error (RRMSE), cardiac trigger timing accuracy, and heart rate estimation error. Additionally, we performed MRI retrospective binning reconstruction using reconstructed ECG reference and evaluated image quality under both standard clinical conditions and challenging scenarios involving arrhythmias and subject motion. To examine scalability of our approach across field strength, the model pretrained on 1.5T data was applied to 3T BPT cardiac acquisitions.</p><p><strong>Main results: </strong>In optimal acquisition scenarios, the reconstructed ECG achieved a median Pearson correlation of 89% relative to the ground truth, while cardiac triggering accuracy reached 94%, and heart rate estimation error remained below 1 bpm. The quality of the reconstructed images was comparable to that of ground truth synchronization. The method exhibited a degree of adaptability to irregular heart rate patterns and subject motion, and scaled effectively across MRI systems operating at different field strengths.</p><p><strong>Significance: </strong>The proposed contactless ECG measurement method has the potential to streamline CMRI workflows, improve patient safety and comfort, mitigate MHD distortion challenges and find a robust clinical application.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fixed point method for PET reconstruction with learned plug-and-play regularization.","authors":"Marion Savanier, Claude Comtat, Florent Sureau","doi":"10.1088/1361-6560/ae05ac","DOIUrl":"10.1088/1361-6560/ae05ac","url":null,"abstract":"<p><p><i>Objective.</i>Deep learning has shown great promise for improving medical image reconstruction, including positron emission tomography (PET). However, concerns remain about the stability and robustness of these methods, especially when trained on limited data. This work aims to explore the use of the Plug-and-Play (PnP) framework in PET reconstruction to address these concerns.<i>Approach.</i>We propose a convergent PnP algorithm for low-count PET reconstruction based on the Douglas-Rachford splitting method. We consider several denoisers trained to satisfy fixed-point conditions, with convergence properties ensured either during training or by design, including a spectrally normalized network and a deep equilibrium model. We evaluate the bias-standard deviation tradeoff across clinically relevant regions and an unseen pathological case in a synthetic experiment and a real study. Comparisons are made with model-based iterative reconstruction, post-reconstruction denoising, a deep end-to-end unfolded network and PnP with a Gaussian denoiser.<i>Main results.</i>Our method achieves lower bias than post-reconstruction processing and reduced standard deviation at matched bias compared to model-based iterative reconstruction. While spectral normalization underperforms in generalization, the deep equilibrium model remains competitive with convolutional networks for PnP reconstruction and generalizes better to the unseen pathology. Compared to the end-to-end unfolded network, it also generalizes more consistently.<i>Significance.</i>This study demonstrates the potential of the PnP framework to improve image quality and quantification accuracy in PET reconstruction. It also highlights the importance of how convergence conditions are imposed on the denoising network to ensure robust and generalizable performance.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Derivation and properties of the convolution model for MRI gradient-induced cardiac stimulation.","authors":"Seung-Kyun Lee, Timothy P Eagan, Desmond Teck Beng Yeo","doi":"10.1088/1361-6560/ae06ec","DOIUrl":"10.1088/1361-6560/ae06ec","url":null,"abstract":"<p><p><i>Objective.</i>Reliable prediction of gradient-induced peripheral nerve stimulation (PNS) and cardiac stimulation (CS) is important to ensure patient safety and maximize imaging performance in modern MRI scanners. Here we extend the dynamic convolution-based PNS prediction model to CS, and present theoretical analysis and numerical survey of general properties of the convolution model.<i>Approach.</i>CS convolution kernel was derived from the exponential model of the strength-duration curve of excitable tissue stimulation with representative stimulation parameters for a whole-body gradient coil. Self-consistency of the convolution method and the properties of the convolution output (response function) for a periodic trapezoidal wave were theoretically analyzed. PNS and CS response functions were computed for clinical 3T brain and pelvic imaging sequences for comparison.<i>Main results.</i>CS convolution kernel takes the form of a simple, decaying exponential function. For both PNS and CS kernels, the convolution model is consistent with the strength-duration curve when applied to a rectangular d<i>G</i>/d<i>t</i>pulse. The long time constant of a CS kernel tends to suppress stimulation by short d<i>G</i>/d<i>t</i>pulses, and makes dynamic CS response correlate more with gradient amplitude than slew rate. On a trapezoidal gradient pulse train, the maximum PNS or CS occurs at the end of the first full slope of the waveform, independent of the number of cycles. In light of the available evidence to the contrary, such independence indicates limitation of the convolution model which is strictly linear.<i>Significance.</i>The proposed CS convolution model can supplement existing PNS models to better assess patient safety of arbitrary gradient waveforms. General theoretical properties of the convolution model can help guide waveform design to minimize risks. While our method was demonstrated primarily on whole-body gradient systems, it can also inform PNS and CS prediction for anatomy-specific scanners employing fast and strong gradient fields.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of nanodosimetric quantities for ion radiotherapy treatment planning based on the degree of association of survival with cluster dose.","authors":"Ramon Ortiz, José Ramos-Méndez, Jian-Hua Mao, Reinhard Schulte, Bruce Faddegon","doi":"10.1088/1361-6560/ae07a3","DOIUrl":"10.1088/1361-6560/ae07a3","url":null,"abstract":"<p><p><i>Objective.</i>To demonstrate potential for a close association between cell survival and cluster dose for ionization parameters (<i>I</i>_p), and to investigate the means to quantify the degree of this association when calculating cluster dose using these nanodosimetric quantities.<i>Approach.</i>The definitions of<i>I</i>_pconsidered were the number of clusters of<i>k</i>or more ionizations per unit track length {<i>C_k, k</i>= 1,…10}. For this<i>I</i>_pdefinition, cluster dose is the number of clusters of<i>k</i>or more ionizations per unit mass. Three sets of published cell survival data, covering a range of clinically relevant particle types and energies, normal and tumor human cells, and aerobic and hypoxic conditions, were used to assess these<i>I</i>_p. Values of<i>C_k</i>were previously calculated for this survival data and evaluated for their application in treatment planning. New to this study, the dependence of cell survival on cluster dose, calculated as local fluence times the mean mass<i>I</i>_p, was used. The degree of association of cell survival with cluster dose was quantified using three statistical methods: the moving window method, the residuals of linear quadratic fit, and the Bayesian information criteria.<i>Results.</i>all three methods identified<i>C</i>₅as the most closely associated with cell survival under aerobic conditions, and<i>C</i>₇under hypoxic conditions, consistent with visual observations. Remarkably,<i>C_k</i>preferred for their close association with cell survival for different particle types having the same fluence, compared to alternative definitions, resulted in a statistically significant closer association of cell survival with cluster dose, regardless of particle fluence.<i>Significance.</i>fluence is a critical property Cluster dose has the potential of supplementing or even replacing RBE-weighted dose in optimization of ion therapy treatment plans. The proposed methodology lays the groundwork for rigorous identification of<i>I</i>_pthat exhibit the highest degree of association of cell survival with cluster dose, a trait that greatly enhances the potential clinical impact of cluster dose.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedFormer: hierarchical medical vision transformer with content-aware dual sparse selection attention.","authors":"Zunhui Xia, Hongxing Li, Libin Lan","doi":"10.1088/1361-6560/ae07a1","DOIUrl":"10.1088/1361-6560/ae07a1","url":null,"abstract":"<p><p><i>Objective</i>. Medical image recognition serves as a key way to aid in clinical diagnosis, enabling more accurate and timely identification of diseases and abnormalities. Vision transformer-based approaches have proven effective in handling various medical recognition tasks. However, these methods encounter two primary challenges. First, they are often task-specific and architecture-tailored, limiting their general applicability. Second, they usually either adopt full attention to model long-range dependencies, resulting in high computational costs, or rely on handcrafted sparse attention, potentially leading to suboptimal performance. To tackle these issues, we present MedFormer, an efficient medical vision transformer with two key ideas.<i>Approach</i>. First, it employs a pyramid scaling structure as a versatile backbone for various medical image recognition tasks, including image classification and dense prediction tasks such as semantic segmentation and lesion detection. This structure facilitates hierarchical feature representation while reducing the computation load of feature maps, highly beneficial for boosting performance. Second, it introduces a novel Dual Sparse Selection Attention (DSSA) with content awareness to improve computational efficiency and robustness against noise while maintaining high performance. As the core building technique of MedFormer, DSSA is designed to explicitly attend to the most relevant content. Theoretical analysis demonstrates that MedFormer outperforms existing medical vision transformers in terms of generality and efficiency.<i>Main results</i>. Extensive experiments across various imaging modality datasets show that MedFormer consistently enhances performance in all three medical image recognition tasks mentioned above.<i>Significance</i>. MedFormer provides an efficient and versatile solution for medical image recognition, with strong potential for clinical application. The code is available onGitHub.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FLASH-enabled proton SBRT for a challenging case of spine metastasis.","authors":"Sophie Wuyckens, Macarena Chocan Vera, Rasmus Nilsson, Viktor Wase, Dario Di Perri, Xavier Geets, John A Lee, Edmond Sterpin","doi":"10.1088/1361-6560/ae023c","DOIUrl":"10.1088/1361-6560/ae023c","url":null,"abstract":"<p><p><i>Objective</i>. The FLASH effect, characterized by potential sparing of organs at risk (OARs) through ultra-high dose rate (DR) irradiation, has garnered significant attention for its capability to address indications previously untreatable at conventional DRs with hypofractionated schemes. While considerable biological research is needed to understand the FLASH effect and determine the FLASH modifying factors (FMF) for individual OARs, treatment planning studies have also emerged. This study evaluates the feasibility of achieving FLASH conditions in proton stereotactic body radiotherapy for spine metastases and establishes the required FMFs under different fractionation regimens.<i>Approach</i>. A conformal FLASH Proton SBRT plan was generated for a patient with spine metastasis in a research version of RayStation11B (RaySearch laboratories AB, Stockholm) on an IBA Proteus Plus system. Two oblique posterior beams were used in the plan. The prescribed dose to the CTV was set according to 3 different fractionation regimens: 5 fractions (fx) of 7 Gy, 8 fx of 5 Gy, and 10 fx of 4.2 Gy. Spot filtering and sorting techniques were applied to maximize the 5% pencil beam scanning DR in the spinal cord (SC). The FLASH effect was assumed to be observed within irradiated regions above 40 Gy s^-1and 4 Gy per fraction.<i>Main results</i>. The generated plans successfully ensure robust target coverage in each fraction. The volume of SC that does not comply with the clinical goal adheres to the FLASH effect conditions in each fraction. Depending on the aforementioned fractionation schemes used, a FMF of approximately 0.6 to 0.8 is necessary to enable such treatment in FLASH conditions.<i>Significance</i>. This study indicates that treating challenging spine metastases with protons using FLASH delivery is technically feasible. However, clinical viability depends on optimistic parameters to trigger the FLASH effect and FMF values below 0.8, which are not yet guaranteed given current research.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144965134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental assessment of novel PET detector components for online imaging of radioactive ion beams.","authors":"Giulio Lovatti, Munetaka Nitta, Francesco Evangelista, Daria Boscolo, Daria Kostyleva, Mohammad Javad Safari, George Dedes, Chiara Gianoli, Beatrice Foglia, Marco Pinto, Han Gyu Kang, Sivaji Purushothaman, Emma Haettner, Christoph Schuy, Christian Graeff, Ulrich Weber, Christoph Scheidenberger, Peter G Thirolf, Taiga Yamaya, Marco Durante, Katia Parodi","doi":"10.1088/1361-6560/ae0674","DOIUrl":"10.1088/1361-6560/ae0674","url":null,"abstract":"<p><p><i>Objective.</i>This work aims to evaluate the ability of novel detector components to measure with submillimeter resolution in beam positron emission tomography (PET) signals produced by¹⁰C and¹¹C radioactive ion beams stopped in PMMA targets and to validate a simulation toolkit for reproducing beam physics and PET detector responses within the framework of the biomedical applications of radioactive ion beam (BARB) project.<i>Approach.</i>The PET system response was assessed by visualizing the radioactive distributions of the beams stopped in tissue surrogate phantoms, and the capacity of the simulation toolkit was evaluated by comparing the experimental results with simulations, both for the depth-dose distribution and PET imaging.<i>Main results.</i>The detector assembly accurately visualized the PET signal with submillimeter resolution, achieving the objective of measuring the difference in the positron range between¹⁰C and¹¹C. The simulation toolkit effectively reproduced the beam characteristics and detector responses, showing a high degree of agreement between the simulated and experimental PET profiles under different beam delivery conditions.<i>Significance.</i>These findings demonstrate the precision and reliability of the novel in-beam PET detector technology and simulation toolkit for small animals, establishing a solid foundation for the second phase of the BARB project, which involves preclinical irradiation of living mice.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145055027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D electroacoustic tomography image enhancement using deep learning with the SAM-Med3D encoder.","authors":"Yankun Lang, Jadon Buller, Yifei Xu, Leshan Sun, Zhuoran Jiang, Shawn Xiang, Lei Ren","doi":"10.1088/1361-6560/ae077d","DOIUrl":"10.1088/1361-6560/ae077d","url":null,"abstract":"<p><p><i>Objective.</i>To overcome the limitations of electroacoustic tomography (EAT) in clinical settings-particularly the artifacts and distortions caused by limited-angle data acquisition-and enable accurate, efficient visualization of electric field distributions for electroporation-based therapies.<i>Approach.</i>We developed a deep learning-based framework that enhances 3D EAT image reconstruction from single-view projections by leveraging the large foundation model (LFM) SAM-Med3D. The encoder was modified into a local-global feature fusion architecture that extracts multi-scale features from intermediate transformer layers, preserving fine structural details while maintaining computational efficiency. A lightweight decoder with progressive up-sampling and skip connections was employed to generate high-resolution images, addressing limitations of conventional U-Net architectures.<i>Main results.</i>We collected a dataset of 50 EAT scans-each with 120 views-for a total of 6000 views. These were acquired from water phantoms and tissue samples under varied electrode configurations and voltages, and split into training (30 scans, 3600 views), validation (10 scans, 1200 views), and testing (10 scans, 1200 views). Our model significantly outperformed baseline 3D U-Nets, achieving an RMSE of 0.0092, PSNR of 41.10, and SSIM of 0.9377. Remarkably, our method reconstructs a full-view 3D EAT image from a single view in just 2 s, demonstrating its potential for near real-time monitoring and adaptive dose verification in electroporation-based therapies.<i>Significance.</i>This is the first application of a LFM like SAM-Med3D for enhancing 3D EAT imaging. The proposed framework addresses the critical challenge of limited-angle data in EAT and demonstrates strong potential for improving precision and safety in electroporation-based therapies, thereby advancing the technique's clinical viability.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning with mechanistic models to optimise radiotherapy and immunotherapy combinations: a proof of concept.","authors":"Allison M Ng, Du Q Huynh, Rebecca A D'Alonzo, Synat Keam, Pejman Rowshanfarzad, Anna K Nowak, Suki Gill, Alistair M Cook, Martin A Ebert","doi":"10.1088/1361-6560/ae0863","DOIUrl":"10.1088/1361-6560/ae0863","url":null,"abstract":"<p><p><i>Objective.</i>To investigate the use of reinforcement learning (RL) algorithms to optimise complex combination cancer therapies. The RL algorithm investigated the effect of varying the radiotherapy (RT) dose in each fraction when administered in conjunction with the immune checkpoint inhibitors (ICIs) anti-PD-1 and anti-CTLA-4.<i>Approach.</i>Data were available for BALB/c mice inoculated with a syngeneic mesothelioma tumour on the flank, treated with combination RT and ICI with tumour growth subsequently measured. A deep<i>Q</i>-network (DQN) and a double DQN were trained using a mechanistic model fitted to the mesothelioma volumes to simulate the dynamics of the tumour microenvironment. Two reward functions were created for the RL algorithm to optimise: the first only considered tumour cell killing, while the second penalised treatment schedules with higher total RT dose. Comparison with experimental results was via the tumour control probability (TCP).<i>Main Results.</i>All the TCPs obtained with the RL algorithm exceeded the TCPs obtained with the same mechanistic model when only 1 or 2 fractions of RT were administered. However, the baseline schedule of 2 Gy per fraction outperformed the treatment schedules generated by RL.<i>Significance.</i>This study highlights the potential for RL to explore the vast solution space of possible treatment schedules, conceivably at the individual patient level.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alvar whole-body model: impact of muscle anisotropy on computational dosimetry.","authors":"Otto Kangasmaa, Tuukka Lehtinen, Ilkka Laakso","doi":"10.1088/1361-6560/adfe31","DOIUrl":"10.1088/1361-6560/adfe31","url":null,"abstract":"<p><p><i>Objective.</i>Computational electromagnetic dosimetry relies on accurate whole-body models to assess human exposure to electromagnetic fields. However, existing models lack anisotropic properties of tissues. This work addresses these limitations by introducing a whole-body model, Alvar, containing fully anisotropic skeletal muscles.<i>Approach.</i>The Alvar model has been constructed based on an anatomic atlas and developed specifically for computational dosimetry. Anisotropic models of skeletal muscles were created using Laplacian vector field simulations. Computational dosimetry was performed using anisotropic and isotropic versions of Alvar and six existing isotropic body models to estimate the electric fields induced due to exposure to spatially uniform magnetic and electric fields at 50 Hz.<i>Main results.</i>Modelling skeletal muscle anisotropy resulted in small variations in the 99th percentile induced electric field values (±5%) when the Alvar model was exposed to a magnetic field. When exposed to an external electric field, using anisotropic muscles systematically decreased the 99th percentile values by 13%. However, local differences in the induced electric field were larger (26%-29%) and even more significant inside muscle tissue (35%-39%).<i>Significance.</i>The results show that isotropic models are sufficient for calculating 99th percentile values when assessing whole-body exposure at power line frequencies but can lead to errors in local electric fields, especially in muscle tissue. This research contributes to the characterisation of uncertainty in low-frequency electromagnetic dosimetry, which can inform the development of human exposure limits.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144965082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}