Physics in medicine and biology最新文献

{"title":"Smooth total variation regularization for interference detection and elimination (STRIDE) for MRI.","authors":"Alexander Mertens, Diego Martinez, Amgad Louka, Ying Yang, Chad Harris, Ian Robert Oliphant Connell","doi":"10.1088/1361-6560/aeb29e","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb29e","url":null,"abstract":"<p><strong>Objective: </strong>MRI is increasingly required to operate near electronic devices that emit dynamic electromagnetic interference (EMI). Existing reference-channel EMI removal methods estimate a transfer function between EMI sensors and imaging coils from the measured data alone, and do not use prior knowledge about the structure of MR images. We develop and validate an EMI removal method that uses the inherent spatial smoothness of MR images as an additional prior, demonstrated on a head-only 0.5T scanner.</p><p><strong>Approach: </strong>Smooth Total variation Regularization for Interference Detection and Elimination (STRIDE) operates in the image domain. For each image column, EMI sensor data are combined and subtracted from the imaging coil data such that the L2 norm of the total variation (TV) along the readout direction is minimized. This exploits the smoothness of MR images, a property well-known in compressed sensing but not previously used in EMI removal. STRIDE was evaluated on phantom and in-vivo brain datasets under EMI scenarios replicating sources encountered at our affiliated hospitals, and compared against two implementations of External Dynamic Interference Estimation and Removal (EDITER).</p><p><strong>Main results: </strong>STRIDE produced visually better EMI removal, higher temporal signal-to-noise ratio, and larger EMI removal percentage than both EDITER implementations. STRIDE also resulted in lower or equal root-mean-square error than both EDITER implementations in all 8 phantom EMI conditions tested. These results held across both phantom and in-vivo datasets.</p><p><strong>Significance: </strong>STRIDE shows that incorporating known properties of MR images into the EMI removal problem provides meaningful improvements over methods that rely on measured data alone. The approach is deterministic, training-free, sequence-independent, and compatible with existing EMI sensor hardware. STRIDE therefore extends reference-channel EMI removal to scenarios where existing methods fail, particularly point-of-care and interventional MRI outside fully shielded environments, providing a complementary path to machine-learning approaches.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148223","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":"DiffINR: conditional neural field diffusion for deformation-driven volumetric image estimation from limited measurements.","authors":"Xiaoxue Qian, Hua-Chieh Shao, You Zhang","doi":"10.1088/1361-6560/aeaaa0","DOIUrl":"10.1088/1361-6560/aeaaa0","url":null,"abstract":"<p><p><i>Objective.</i>We propose DiffINR for rapid deformation-driven estimation of patient-specific target volumes from limited measurements. DiffINR uses a coordinate-based implicit neural representation (INR) to estimate a dense deformation vector field (DVF) that warps a high-quality prior volume (source) to the target anatomy, with patient-specific INR initialization provided by a population-based conditional hyperdiffusion model.<i>Approach.</i>During offline training, INRs were optimized on fully sampled source-target image volume pairs to represent DVFs. A Transformer diffusion model, hyperdiffusion, learned the distribution of the resulting INR weights. The model was jointly conditioned on anatomical features extracted from the training source and target volumes by a foundation model and on features encoded directly from limited measurements. At inference, hyperdiffusion generated INR initial weights, which were further fine-tuned by patient-specific optimization using measurement-domain fidelity and DVF-smoothness losses. We evaluated DiffINR on two tasks: cone-beam computed tomography (CBCT) estimation from limited-angle x-ray projections and real-time volumetric magnetic resonance imaging (MRI) estimation from ultra-sparse k-space data.<i>Main Results.</i>For orthogonal-view 90° CBCT estimation, DiffINR achieved a structural similarity index measure (SSIM) of 0.962 ± 0.021, relative error of 6.84% ± 1.70%, and target registration error of 3.61 ± 1.27 mm, outperforming the iterative methods, population-based models, and randomly initialized INR baselines. For volumetric MRI estimation with 13 radial spokes per slice, DiffINR achieved an SSIM of 0.985 ± 0.007, Dice coefficient of 0.89 ± 0.04, and center-of-mass error of 1.30 ± 1.09 mm. DiffINR showed high robustness to data distribution shifts at test time, including dataset changes, limited-angle setting variations, and different MRI sampling sparsity levels. Test-time runtime was 23 s for CBCT estimation and 27 s for MRI estimation, compared with 795 s and 928 s, respectively, for INR optimization from scratch.<i>Significance.</i>By combining hyperdiffusion-based INR initialization with patient-specific data consistency, DiffINR achieves fast and accurate deformation-driven volumetric image estimation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017818","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":"Anatomy-prior-guided and scan-aware 3D segmentation of the pediatric pancreas in CT images.","authors":"Wenbo Xiong, Nian Liu, Hanran Yan, Yan Liu","doi":"10.1088/1361-6560/aea8c5","DOIUrl":"10.1088/1361-6560/aea8c5","url":null,"abstract":"<p><p><i>Objective.</i>Pediatric pancreas CT segmentation is challenging because developmental anatomy and scan-protocol variability jointly affect organ size, image appearance, and boundary visibility. This study evaluated whether explicitly incorporating anatomy-related and scan-related case-level descriptors could improve pediatric pancreas segmentation relative to an image-only baseline.<i>Approach.</i>We developed APSC-Net, an anatomy-prior-guided and scan-aware 3D segmentation framework based on SegResNet. Structured anatomy-related and scan-related descriptors were encoded as separate context vectors and injected through bottleneck anatomy-prior guidance and decoder-stage low-rank FiLM modulation. The model was trained and internally tested on GE pediatric CT cases, then evaluated without retraining on a held-out Siemens subset in a cross-vendor evaluation within the same cohort and institution.<i>Main results.</i>On the full held-out GE test set, APSC-Net improved the mean dice similarity coefficient (DSC) from 0.665 to 0.770 and reduced the 95% Hausdorff distance (HD95) from 22.2 to 11.6 mm compared with an image-only SegResNet baseline. Stratified analysis showed DSC gains across pediatric age groups, including the youngest subgroup (0.718-0.751 for 0-5 years), and across exposure-stage strata. In the GE stream ablation, the full and scan-only configurations performed similarly. In secondary testing on held-out Siemens cases, the full model achieved lower average symmetric surface distance (ASSD) than either single-stream variant and achieved the lowest HD95 and ASSD among the compared methods, while age-group domain-regularized baselines remained competitive in DSC.<i>Significance.</i>On the evaluated cohorts, APSC-Net achieved lower boundary-distance errors than the image-only SegResNet baseline. The findings support further evaluation of case-level metadata conditioning for pediatric imaging tasks affected by developmental and acquisition-related variability.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148983391","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":"Deep learning-based prediction of diffusion MRI responses from microvascular OCT angiograms.","authors":"Jaloliddin Rustamov, Zahiriddin Rustamov, Frédéric Lesage, Nazar Zaki, Rafat Damseh","doi":"10.1088/1361-6560/aeaa9f","DOIUrl":"10.1088/1361-6560/aeaa9f","url":null,"abstract":"<p><p><i>Objective.</i>Diffusion magnetic resonance imaging (MRI) signals are sensitive to microvascular geometry and hemodynamics, but current approaches rely on computationally intensive simulations on optical coherence tomography (OCT)-derived vascular graphs. We investigated whether three-dimensional deep learning models can serve as fast surrogates that predict diffusion MRI responses directly from microvascular OCT angiograms.<i>Approach.</i>Five mouse cortical OCT angiograms were segmented into 4792 vascular subgraphs and converted into nine-channel image volumes encoding vascular geometry, magnetic field gradient, hematocrit, oxygen saturation, partial oxygen pressure, blood velocity magnitude, and three velocity components. For each sample, a physics-based framework generated spin echo (SE) and diffusion-weighted signals for three-values (50, 100, 500 s mm), three gradient timing pairs, and ten gradient directions. We trained five 3D architectures (AutoEncoder, BasicUNet, DenseNet169, EfficientNetB4 and ResNet) with late fusion of acquisition parameters and evaluated performance using MSE, MAE and.<i>Main results.</i>Under within-distribution (random-split) evaluation, testvalues were at least 0.87. DenseNet169 achieved the best overall accuracy (MSE, MAE,) and maintainedacross all diffusion settings, while ResNet provided near-perfect reconstruction of the SE sequence alone (). Leave-one-subject-out cross-validation gave MSE, MAE, and, with 34.4% of held-out samples reaching. Channel ablation revealed velocity magnitude and velocity components as dominant predictors. Once trained, the models predicted full diffusion responses in milliseconds, providing speedups of four to six orders of magnitude compared with simulations.<i>Significance.</i>These results demonstrate that deep learning surrogates can accurately and efficiently approximate diffusion MRI responses from microvascular OCT angiograms, enabling rapid exploration of microvascular configurations and acquisition protocols.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017770","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":"Uncertainty-aware registration for probabilistic margin assessment in renal cell carcinoma ablation.","authors":"Libin Liang, Shuwei Xing, Suzanne M Wong, Derek W Cool, Aaron Fenster","doi":"10.1088/1361-6560/aeb2a1","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb2a1","url":null,"abstract":"<p><strong>Objective: </strong>Quantitative assessment of renal cell carcinoma (RCC) ablation margins requires reliable registration of pre- and post-ablation images. However, ablation-related tissue changes weaken local anatomical correspondence, making deformation estimates potentially non-unique. We developed an uncertainty-aware registration framework for more reliable margin estimation.&#xD;Approach: An ensemble of plausible registrations was generated by stochastically sampling key registration settings, including the registration mask, similarity metric, B-spline grid spacing, and initial alignment, with Latin hypercube sampling used to improve parameter-space coverage. Registration uncertainty was characterized from this ensemble and propagated into probabilistic margin estimation. Evaluation used 25 biomechanically simulated cases with known deformation fields and 47 clinical RCC cases.&#xD;Main results: In simulation, 98 ± 2% of kidney voxels had a mean displacement error below 1 mm, and uncertainty was strongly associated with registration error (median case-wise Spearman correlation, 0.99). In the clinical study, our method achieved anatomical alignment consistent with the deterministic baselines, with a median kidney Dice similarity coefficient of 0.944 and a median local mean absolute surface distance of 0.5 mm. In general, our method maintained a sensitivity of 1.00 while achieving higher positive predictive value (PPV) than the deterministic methods. Under the global criterion, PPV was 0.70 versus 0.54-0.58, although the pairwise differences between our method and each deterministic method were not statistically significant. Under the focal criterion, PPV was 0.78 versus 0.29-0.50. The pairwise differences were significant between our method and the standard and penalty-constrained B-spline methods (both p<0.001), but not between our method and the diffeomorphic method (p=0.063). &#xD;Significance: The framework extends registration uncertainty estimation to RCC ablation-margin assessment by converting variability in plausible correspondences into probabilistic margin maps. It highlights negative-margin regions consistently identified across registrations, reduces registration-sensitive false-positive local tumor progression predictions, and may support more reliable postoperative evaluation.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148802","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":"MPISuperRes-PnP: a super-resolution zero-shot plug-and-play reconstruction algorithm for magnetic particle imaging.","authors":"Vladyslav Gapyak, Thomas März, Andreas Weinmann","doi":"10.1088/1361-6560/aea518","DOIUrl":"10.1088/1361-6560/aea518","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic particle imaging (MPI) is a promising, emerging medical imaging modality. MPI is based on the non-linear response of magnetic nanoparticles to an applied magnetic field and does not expose the specimen to ionizing radiation. The measured signal is the voltage induced in receive coils by the particles' response. Reconstructing the particle concentration from the signal constitutes the imaging task. Even using state-of-the-art measurement-based reconstruction, the associated spatial grid is very coarse, hence super-resolution techniques are important. In this work, we propose an approach for super-resolution in MPI inspired by energy minimization.<i>Approach.</i>Different methods have been proposed for super-resolution in MPI, ranging from upscaling of the associated system matrix to interpolation of the reconstruction. Here we incorporate super-resolution into the reconstruction task via an energy minimization formulation. Following the plug-and-play (PnP) approach to energy minimization we derive a splitting scheme where the arising Gaussian denoising task is treated with a pre-trained learned Gaussian denoiser.<i>Main results.</i>We derive a super-resolution method for MPI based on a PnP approach using a pre-trained denoiser in zero-shot fashion. This way, we incorporate benefits of deep learning without training and avoid the need of training data. Further, we provide a quantitative and qualitative evaluation of the proposed method on simulated data with realistic noise. Hyper-parameters are selected via an extended parameter search. The found parameters are applied for reconstruction on real data. We qualitatively show the applicability of our method on real data (MPIData: EquilibriumModelWithAnisotropy and 2D-OpenMPI Data).<i>Significance.</i>The proposed method employs a deep-learning denoiser without training-thus it does not require presently scarcely available MPI training data. The denoiser behaves conservatively, i.e. no hallucination artifacts were observed. The super-resolution approach is generic such that it can be applied in future MPI contexts involving different regularizers or different imaging tasks.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148920526","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":"Domain-aligned self-supervision: CT-specialized DINOv3 enhances PET/CT tumor segmentation.","authors":"Yu-Nong Scarlett Lin, Shansong Wang, Yuheng Li, Mojtaba Safari, Qiang Li, Zach Eidex, Yizhou Wu, Ryan James Sanford, Kevin T Chen, Xiaofeng Yang","doi":"10.1088/1361-6560/aeb29f","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb29f","url":null,"abstract":"<p><strong>Objective: </strong>This study investigates how domain-aligned self-supervised pretraining influences downstream PET/CT tumor segmentation, with a focus on disentangling the roles of domain proximity and pretraining dataset scale.&#xD;&#xD;Approach: We compared nnU-Net segmentation models initialized from random weights or from DINOv3 encoders pretrained on natural images, X-ray mammography, CT, or PET, using datasets of different sizes. To disentangle the effects of pretraining domain and dataset size, CT-DINOv3 encoders were pretrained using different numbers of 2D CT slices, including subsets matched in size to the pretraining datasets from other domains. Models were evaluated on two PET/CT benchmarks, autoPET (whole-body, FDG/PSMA) and HECKTOR (head-and-neck primary tumors and metastatic lymph nodes), using five-fold cross-validation with frozen encoders.&#xD;&#xD;Main results: CT-pretrained models achieved the highest Dice scores for lymph nodes, FDG lesions, and PSMA lesions. For primary tumors, PET pretraining produced the highest observed Dice score, followed by CT pretraining, suggesting the potential benefit of in-domain representations when PET data are sufficient. Notably, PET pretraining exceeded CT pretraining using equally sized datasets for PSMA but remained below models pretrained on larger CT datasets for both PSMA and FDG targets. In contrast, natural-image pretraining did not outperform from-scratch training despite its much larger pretraining scale, indicating that scale alone is insufficient when domain proximity is low. Across comparisons, strong transfer reflected the combination of domain proximity and sufficient pretraining scale, especially for CT-based models with broad data availability.&#xD;&#xD;Significance: These results demonstrate that both domain proximity and dataset scale jointly determine the effectiveness of self-supervised pretraining. While higher domain alignment improves performance at comparable scale, sufficient data is required to realize this benefit. CT-based pretraining provides a favorable balance between domain relevance and data availability, leading to robust performance across tasks. This study provides practical guidance for selecting pretraining strategies in PET/CT segmentation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148277","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 radiotherapy for electrons and protons: roadmap towards the clinical introduction.","authors":"Anna L Petoukhova, Sébastien Penninckx, Jan Schuemann, Fabio Di Martino, Fabiola Paiar, Anouk Sesink, Felipe A Calvo, Francesco Romano, Giuliana Milluzzo, Marco Schwarz, Steven Habraken, Mahbubur Ronny Rahman, Charles B Simone Ii, Kees Spruijt, Haibo Lin, Mandy Kiderlen, Antoine Desmet, Michele M Kim, Anthony Mascia","doi":"10.1088/1361-6560/aeb29d","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb29d","url":null,"abstract":"<p><p>Radiation therapy (RT) is a crucial component of cancer management, with most patients receiving RT at some point, often in combination with surgery or systemic treatments. Cell sparing at ultrahigh dose rates was found in in vitro cell survival fraction experiments dating back to the 1960s. In the 1970s, studies revealed an unexpected dose-rate effect on radiation-induced mortality in mice and a dose-rate-dependent response in rat skin. This effect was assumed to be affecting healthy and tumour tissue equally and was not pursued further. Decades later, this modality of RT was rediscovered using ultra-high dose-rate irradiation (average dose rate >40 Gy/s) and it was demonstrated that this improves the therapeutic ratio, sparing healthy tissue while being isoeffective for tumours. This effect is referred to as the FLASH effect. Currently, the first human trials using electron and proton beams are ongoing, although significant challenges remain in fully understanding the radiobiological and chemical mechanisms behind this phenomenon. This roadmap provides an overview of the current and future challenges of FLASH radiation therapy and outlines research directions to optimally advance the field and design future clinical trials: radiobiology of the FLASH effect, opportunities and beam parameters for the future in FLASH with electrons, intraoperative FLASH radiotherapy with electrons, ultra-high dose-rate dosimetry, opportunities for the future in FLASH with protons, FLASH treatment planning and machine QA with protons, clinical translation of FLASH with electrons and first experiences and challenges of initial proton FLASH trials.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148721","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":"Simultaneously quantifying<i>K</i>₁,<i>k</i>₂, and blood volume in myocardial imaging with overlapping volumes of interest: practical identifiability.","authors":"Dale J Stentz, Marie A Guerraty, Scott D Metzler","doi":"10.1088/1361-6560/ae62c5","DOIUrl":"10.1088/1361-6560/ae62c5","url":null,"abstract":"<p><p><i>Objective.</i>Dynamic imaging in emission computed tomography allows the determination of physiological parameters that have important clinical implications. In myocardial imaging, three parameters of interest are the uptake and washout rates,and, respectively, and the blood-volume fraction,. Theoretically, these parameters can be determined from time-activity curves (TACs) of the blood pool and the myocardium from injection and uptake of a perfusion tracer, if the matrix describing the relationship between the true TACs and the observed TACs is known. Herein, we numerically confirm these theoretical predictions and study the impact of experimental noise and matrix element uncertainty on these three parameters.<i>Approach.</i>Using a one-compartment model, we simulated TACs for different values of,, and. A mixing matrix involving recovery coefficients and spillover fractions was used to describe the transformation of the true concentration to the observed concentrations. Kinetic model fitting was employed to measure the three physical parameters, and we measured the bias of these fit results with respect to the true simulated values. We investigated a range of mixing matrices, rates of uptake and washout, and blood volume fractions.<i>Main results.</i>We confirmed the theoretical prediction that,, andcan be simultaneously measured accurately if and only if all elements of the mixing matrix are known. There was an approximately linear effect of uncertainty in the measurement of the three physical parameters to the noise level on the TACs. Consistent with previous results, greater mixing via the mixing matrix or the blood-volume fraction resulted in greater uncertainty.<i>Significance.</i>This work shows we can accurately measure the physical parameters likewhile also quantifying the uncertainty in their measurement due to noise on the TACs and mixing-matrix uncertainty.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147778591","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":"Study of three-dimensional dose distribution measurement of ultra-high dose rate electron beam.","authors":"Wei Zhang, Chang Cheng, Ronghu Mao, Leijie Ma, Dehong Li, Zhihua Peng, Keying Zhu, Wenjun Liu, Gaolong Zhang, Suyalatu Zhang, Xiaoyu Xu, Weiwei Qu","doi":"10.1088/1361-6560/aeabeb","DOIUrl":"10.1088/1361-6560/aeabeb","url":null,"abstract":"<p><p><i>Objective</i>. FLASH radiotherapy which employs an ultra-high dose rate (UHDR) (Gy s), effectively protects normal tissues and organs while maintaining robust tumor control. However, it also presents serious challenges for quality assurance (QA). Currently, dosimeters face various limitations under FLASH radiotherapy conditions which make it difficult to meet the demand for rapid and high-precision three-dimensional (3D) dose verification. In this paper, CMOS cameras were used to capture luminescence images generated by irradiated water and for 3D dose reconstruction.<i>Approach</i>. A tri-orthogonal view camera imaging setup was designed and constructed to collect radioluminescence signals in water phantom. The ordered subsets expectation maximization iterative algorithm was adopted to obtain 3D dose reconstruction. The experiments were performed by 6 MV and 10 MV conventional dose rate x-rays, as well as 9 MeV electron with an UHDR of 120 Gy s. The dose distribution was verified by treatment planning system calculations and film measurements, respectively. Quantitative evaluations were performed through percentage depth dose (PDD), lateral dose profiles, and two-dimensional gamma analysis (3%/3 mm, 10% threshold).<i>Results</i>. The luminescence intensity of water phantom shows high linearity with the irradiation dose and exhibits independent characteristics with dose rate. The PDD curve of conventional x-ray reconstruction underestimates the dose in the dose fall-off region, and the transverse dose distribution shows high consistency with the reference distribution. Under different irradiation fields, the difference in the full width at half maximum (FWHM) of the lateral dose distribution curves at each depth is less than 1.25 mm. The PDD curve of the UHDR electron beam reconstruction result underestimates the dose in the shallow region. The deviation of the half-peak dose depth from the film measurement result is less than 0.7 mm. Calculations of the gamma passing rate on each plane of the reconstruction result indicate that the method used shows high accuracy for the transverse distribution.<i>Significance</i>. The proposed 3D dose distribution measurement method is applicable to both conventional dose rate radiotherapy and UHDR radiotherapy conditions. It demonstrates favourable performance when evaluating lateral parameters including field shape, field size, flatness and uniformity, which is promising for field consistency verification in clinical UHDR radiotherapy QA.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149031163","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}