Physics in medicine and biology最新文献

{"title":"Rapid left ventricle mesh prediction by adaptive deformable model fitting.","authors":"Yurun Yang, Yang He, Dong Liang, Yanjie Zhu","doi":"10.1088/1361-6560/adc237","DOIUrl":"10.1088/1361-6560/adc237","url":null,"abstract":"<p><p><i>Objective.</i>Accurate three-dimensional left ventricular mesh reconstruction from medical imaging plays a pivotal role in critical clinical applications such as cardiac biomechanical simulations, myocardial strain quantification, and pathological characterization. This study aims to overcome key limitations in existing approaches including the computational complexity of conventional finite element modeling, the heavy reliance on large-scale paired training data in deep learning methods, and limited generalizability across diverse cardiac pathologies.<i>Approach.</i>We present a novel adaptive deformable model fitting framework for rapid and training-free ventricular mesh prediction, incorporating two core components: (1) an adaptive mesh module leveraging proper orthogonal decomposition-derived basis functions, and (2) a two-stage fitting scheme that independently optimizes endocardial/epicardial surfaces through shared modal components. Our methodology eliminates dependence on annotated datasets through adaptive mesh basis functions derived via proper orthogonal decomposition, dynamically scaled across orthogonal spatial dimensions to accommodate inter-patient morphological variations in adaptive mesh module. The two-stage fitting scheme independently optimizes endocardial and epicardial surfaces using shared modal components while preserving anatomical topology and addressing myocardial wall thickness heterogeneity. The overall framework integrates differentiable voxelization and polyharmonic spline interpolation to achieve gradient-driven alignment between predicted meshes and segmentation masks.<i>Main Results.</i>Comprehensive validation across three cardiac magnetic resonance imaging datasets ,which demonstrated performance with a mean Dice coefficient of 0.85. In the clinical data set of dilated cardiomyopathy diseases, the dice value of our method averaged 0.78, which demonstrated 16% higher than that of other methods.<i>Significance.</i>This work further improves the accuracy of three-dimensional left ventricle fitting and enhances inference speed. The proposed approach demonstrates significant advantages by eliminating the need for additional training datasets while maintaining strong generalizability across various cardiac pathologies.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657231","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":"FPGA-based digitizer for BGO-based time-of-flight PET.","authors":"Daehee Lee, Sun Il Kwon","doi":"10.1088/1361-6560/adc362","DOIUrl":"10.1088/1361-6560/adc362","url":null,"abstract":"<p><p>We present a novel field-programmable gate array (FPGA)-based bismuth germanate (BGO) time-of-flight (TOF) digitizer, implemented on an FPGA (XC7VX485T-2FFG1761C, Xilinx). This digitizer is designed to address the recently highlighted characteristics of BGO, which generates both scintillation and prompt Cerenkov photons when a 511 keV photon interacts with BGO. The developed digitizer independently processes these two types of photons for precise energy and timing measurements. The digitizer incorporates a noise-resistant binary counter that measures energy signals using the time-over-threshold (TOT) method. For timing measurements, we employ an embedded dual-side monitoring time-to-digital converter, which efficiently captures timing information while maintaining low resource usage. We validated the efficacy of our FPGA-based TOF digitizer through extensive experiments, including both electrical testing and coincidence measurements using BGO pixels. Our evaluations of TOT energy and timing performance utilized two 3 × 3 × 20 mm³BGO pixels coupled to CHK-HD MT silicon photomultipliers. The digitizer achieved a coincidence timing resolution (CTR) of 407 ps full width at half maximum (FWHM) for events within the full width at tenth maximum of the photopeak in the measured TOT energy spectrum. Notably, when measured with an oscilloscope, the same detector pair exhibited a CTR of 403 ps FWHM, confirming that the performance of the developed digitizer is comparable to that of an oscilloscope. With its low resource usage, our design offers significant potential for scalability, making it particularly promising for multi-channel BGO-based PET systems.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670594","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":"Optimizing<i>in vivo</i>data acquisition for robust clinical microvascular imaging using ultrasound localization microscopy.","authors":"Chengwu Huang, U- Wai Lok, Jingke Zhang, Xiang Yang Zhu, James D Krier, Amy Stern, Kate M Knoll, Kendra E Petersen, Kathryn A Robinson, Gina K Hesley, Andrew J Bentall, Thomas D Atwell, Andrew D Rule, Lilach O Lerman, Shigao Chen","doi":"10.1088/1361-6560/adc0de","DOIUrl":"10.1088/1361-6560/adc0de","url":null,"abstract":"<p><p><i>Objective</i>. Ultrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity, the number of detected MBs, and signal-to-noise ratio (SNR), all of which vary in clinical scenarios involving bolus MB injections. These sources of variations underscore the need to optimize MB dosage, data acquisition timing, and imaging settings in order to standardize and optimize ULM of microvasculature. This pilot study aims to investigate the temporal changes in MB signals during bolus injections in both pig and human models to optimize data acquisition for clinical ULM.<i>Approach.</i>Quantitative indices, mainly including individual MB SNR, normalized cross-correlation (NCC) of the MB signal with the point-spread function, and the number of localizable MBs, were developed to evaluate MB signal quality and guide the selection of acquisition timing. The effects of transmitted voltage and dosage on signal quality for MB localization were also explored.<i>Main results</i>. In both pig and human studies, MB localization quality (primarily indicated by NCC) reached a minimum at peak MB concentration, then improved as MB counts decreased during the wash-out phase. An optimal acquisition window was identified by balancing localization quality (empirically, NCC > 0.57) and MB concentration. In the pig model, a relatively short time window (approximately 10 s) for optimal acquisition was identified during the rapid wash-out phase, highlighting the need for real-time MB signal monitoring during data acquisition. The slower wash-out phase in humans allowed for a more flexible imaging window of 1-2 min, while trade-offs were observed between localization quality and MB density (or acquisition length) at different wash-out phase timings. Guided by these findings, robust ULM imaging was achieved in both pig and human kidneys using a short period of data acquisition (3.6 s and 9.6 s of data), demonstrating its feasibility in clinical practice.<i>Significance.</i>This study provides insights into optimizing data acquisition for consistent and reproducible ULM, paving the way for its standardization and broader clinical applications.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630845","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":"Radiation damage and recovery of plastic scintillators under ultra-high dose rate 200 MeV electrons at CERN CLEAR facility.","authors":"Cloé Giguère, Alexander Hart, Joseph Bateman, Pierre Korysko, Wilfrid Farabolini, Yoan LeChasseur, Magdalena Bazalova-Carter, Luc Beaulieu","doi":"10.1088/1361-6560/adc234","DOIUrl":"10.1088/1361-6560/adc234","url":null,"abstract":"<p><p><i>Objective.</i>The FLASH effect holds significant potential in improving radiotherapy treatment outcomes. Very high energy electrons (VHEEs) with energies in the range of 50-250 MeV can effectively target tumors deep in the body and can be accelerated to achieve ultra-high dose rates (UHDR), making them a promising modality for delivering FLASH radiotherapy in the clinic. However, apart from suitable VHEE sources, clinical translation requires accurate dosimetry, which is challenging due to the limitation of standard dosimeters under UHDR conditions. In this study, water-equivalent and real-time plastic scintillation dosimeters (PSDs) are tested to evaluate their viability for FLASH VHEE dosimetry.<i>Approach.</i>A 4-channel PSD, consisting of polystyrene-based BCF12 and Medscint proprietary scintillators, polyvinyltoluene-based EJ-212 and a blank plastic fiber channel for Cherenkov subtraction was exposed to the 200 MeV VHEE UHDR beam at the CLEAR CERN facility. The Hyperscint RP200 platform was used to assess linearity to dose pulses of up to 90 Gy and dose rates up to4.6×109Gy s^-1, and to investigate radiation damage and recovery after dose accumulation of 37.2 kGy.<i>Main</i><i>results.</i>While blank fiber response was linear across the entire dose range studied, light output saturated above 45 Gy/pulse for scintillators. Despite radiation damage, linearity was preserved, though it resulted in a decrease of scintillator and blank fiber light output of<1.87%/kGy and a shift in spectra towards longer wavelengths. Short-term recovery (<100 h) of these changes was observed and depended on rest duration and accumulated dose. After long-term rest (<172 days), light output recovery was partial, with 6%-22% of residual permanent damage remaining, while spectral recovery was complete.<i>Significance.</i>We showed that PSDs are sensitive to radiation damage, but maintain dose linearity even after a total accumulated dose of 37.2 kGy, and exhibit significant response recovery. This work highlights the potential of PSDs for dosimetry in UHDR conditions.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657174","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 techniques for proton dose prediction across multiple anatomical sites and variable beam configurations.","authors":"Ivan Vazquez, Danfu Liang, Ramon M Salazar, Mary P Gronberg, Carlos Sjogreen, Tyler D Williamson, X Ronald Zhu, Thomas J Whitaker, Steven J Frank, Laurence E Court, Ming Yang","doi":"10.1088/1361-6560/adc236","DOIUrl":"10.1088/1361-6560/adc236","url":null,"abstract":"<p><p><i>Objective.</i>To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, with a focus on scenarios involving limited or highly heterogeneous datasets.<i>Approach.</i>In this study, 541 prostate and 632 head and neck (H&N) proton therapy plans were used to train and evaluate convolutional neural networks designed for the task of dose prediction. Datasets were grouped by anatomical site and beam configuration to assess the impact of beam masks-graphical depictions of radiation paths-as a model input. We also evaluated the effect of combining datasets. Model performance was measured using dose-volume histograms (DVHs) scores, mean absolute error, mean absolute percent error, dice similarity coefficients (DSCs), and gamma passing rates.<i>Main results.</i>DSC analysis revealed that the inclusion of beam masks improved dose prediction accuracy, particularly in low-dose regions and for datasets with diverse beam configurations. Data aggregation alone produced mixed results, with improvements in high-dose regions but potential degradation in low-dose areas. Notably, combining beam masks and data aggregation yielded the best overall performance, effectively leveraging the strengths of both strategies. Additionally, the magnitude of the improvements was larger for datasets with greater heterogeneity, with the combined approach increasing the DSC score by as much as 0.2 for a subgroup of H&N cases characterized by small size and heterogeneity in beam arrangement. DVH scores reflected these benefits, showing statistically significant improvements (<i>p</i>< 0.05) for the more heterogeneous H&N datasets.<i>Significance.</i>Artificial intelligence-based dose prediction models incorporating beam masks and data aggregation significantly improve accuracy in proton therapy planning, especially for complex cases. This technique could accelerate the planning process, enabling more efficient and effective cancer treatment strategies.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656891","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":"Adaptive radiotherapy triggering for nasopharyngeal cancer based on bayesian decision model.","authors":"Long Yang, Xiaojie Yin, Zhenhao Li, Zhiyu Ding, Yue Zou, Ziwei Li, Enwei Mo, Qingyuan Zhou, Jiazhou Wang, Weigang Hu","doi":"10.1088/1361-6560/adc238","DOIUrl":"10.1088/1361-6560/adc238","url":null,"abstract":"<p><p><i>Objective.</i>To develop a Bayesian decision model for adaptive radiotherapy (ART) in nasopharyngeal cancer (NPC) that balances clinical capacity of ART and inter-fraction dosimetric changes.<i>Approach.</i>A retrospective analysis was conducted on 84 fractions from 17 NPC patients treated with intensity-modulated radiotherapy using a CT-Linac. Fourteen patients were included for the model construction, and three for validation. Daily diagnostic-level CT images were rigidly registered to the planning CT for regions of interest and treatment plan propagation. The propagated contours were reviewed and refined by radiation oncologists. For each daily CT, percentage differences in 27 dose metrics were compared to the original plan. Composite scores of dose differences were developed using factor analysis on planning target volume (PTV) and organ at risk (OAR) dose metrics. These scores were integrated into a Bayesian decision model, which incorporated a subjective trigger rate to determine the initiation of ART.<i>Main results.</i>The model generated individualized re-plan strategies based on composite scores for PTV or OAR, with trigger rates ranging from 10% to 60%. In the validation with 14 fractions, significant anatomical and dosimetric variations were identified. At a 30% trigger rate, only one fraction was misclassified.<i>Significance.</i>It is feasible to employ a Bayesian decision model for ART, merging subjective clinical insights with objective dosimetric data to refine re-planning decisions.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656881","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":"Monte Carlo simulation of beam quality correction factor<i>k_Q</i>for carbon-ion beams using FLUKA and GATE for selected cylindrical and plane-parallel ionization chambers.","authors":"Jessica Stolzenberg, Pascal Saße, Yuri Simeonov, Bjoern Poppe, Kilian-Simon Baumann, Hui Khee Looe","doi":"10.1088/1361-6560/adc5d5","DOIUrl":"https://doi.org/10.1088/1361-6560/adc5d5","url":null,"abstract":"<p><strong>Objective: </strong>To benchmark Monte Carlo codes FLUKA and GATE/Geant4 regarding the beam quality correction factors of ionization chambers for monoenergetic carbon-ion beams against experimental results by Holm et al. (2022).</p><p><strong>Approach: </strong>Monte Carlo codes FLUKA and GATE/Geant4 were used to simulate the beam quality correction factor<i>k_Q</i>for one plane-parallel (PTW 34001) and two cylindrical ionization chambers (PTW 30013 and IBA FC65-G) using two monoenergetic carbon-ion beams and an energy modulated beam in accordance with Holm et al. (2022). Additionally, chamber-specific factor<i>f_Q</i>and perturbation factor<i>p_Q</i>were calculated. Differences between Geant4 reference physics lists were investigated by comparing simulated depth dose distributions,<i>f_Q</i>, and<i>p_Q</i>values for 429 MeV/u.</p><p><strong>Main results: </strong>Simulated<i>k_Q</i>factors were found to differ from experimentally determined<i>k_Q</i>factors of Holm et al. (2022) by 2.5% for cylindrical chambers, whereas the plane-parallel chamber showed larger deviations of 3.1/2.6% (GATE/FLUKA), exceeding the simulation uncertainty of 1.7%.<i>f_Q</i>and<i>p_Q</i>factors simulated using different Geant4 physics lists were comparable within the Type-A uncertainty of 0.2%. Nevertheless, the depth dose curves for physics lists using the INCL++ model showed an increase in dose at all depths except for the fragmentation tail. Differences in<i>f_Q</i>factors between Monte Carlo codes FLUKA and GATE of up to 1.8% have been observed.</p><p><strong>Significance: </strong>More investigations are needed to understand the cause of the observed deviations between experimental results and Monte Carlo calculations of beam quality correction factor<i>k_Q</i>. No statistically significant differences are observed between investigated Geant4 physics lists for<i>f_Q</i>,<i>k_Q</i>, and<i>p_Q</i>simulations. Notably, differences between Monte Carlo codes FLUKA and GATE are one of the main sources that limit the current simulation uncertainty.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143731290","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 segmentation of head and neck organs at risk on CBCT images with dosimetric assessment for radiotherapy.","authors":"Lucía Cubero, Cédric Hémon, Anaïs Barateau, Joël Castelli, Renaud de Crevoisier, Oscar Acosta, Javier Pascau","doi":"10.1088/1361-6560/adbf63","DOIUrl":"10.1088/1361-6560/adbf63","url":null,"abstract":"<p><p><i>Objective.</i>Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is still a challenge due to the poor soft tissue contrast, artifacts, and limited field-of-view of these images, alongside the lack of large, annotated datasets to train deep learning (DL) models. This study aims to develop a comprehensive framework to segment 25 HN OARs on CBCT to facilitate treatment replanning.<i>Approach.</i>The proposed framework was developed in three steps: (i) refining an in-house framework to segment 25 OARs on CT; (ii) training a DL model to segment the same OARs on synthetic CT (sCT) images derived from CBCT using contours propagated from CT as ground truth, integrating high-contrast information from CT and texture features of sCT; and (iii) validating the clinical relevance of sCT segmentations through a dosimetric analysis on an external cohort.<i>Main results.</i>Most OARs achieved a dice score coefficient over 70%, with mean average surface distances of 1.30 mm for CT and 1.27 mm for sCT. The dosimetric analysis demonstrated a strong agreement in the mean dose and D2 (%) values, with most OARs showing non-significant differences between automatic CT and sCT segmentations.<i>Significance.</i>These results support the feasibility and clinical relevance of using DL models for OAR segmentation on both CT and CBCT for HNC RT.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143605737","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":"HWA-ResMamba: automatic segmentation of coronary arteries based on residual Mamba with high-order wavelet-enhanced convolution and attention feature aggregation.","authors":"Jinzhong Yang, Peng Hong, Lu Wang, Lisheng Xu, Dongming Chen, Chengbao Peng, An Ping, Benqiang Yang","doi":"10.1088/1361-6560/adc0dd","DOIUrl":"10.1088/1361-6560/adc0dd","url":null,"abstract":"<p><p><i>Objective.</i>Automatic segmentation of coronary arteries is a crucial prerequisite in assisting in the diagnosis of coronary artery disease. However, due to the fuzzy boundaries, small-slender branches, and significant individual variations, automatic segmentation of coronary arteries is extremely challenging.<i>Approach.</i>This study proposes a residual Mamba with high-order wavelet-enhanced convolution and attention feature aggregation (HWA-ResMamba) for coronary arteries segmentation. The network consists of three core modules: high-order wavelet-enhanced convolution block (HWCB), residual Mamba (ResMamba), and attention feature aggregation (AFA) module. Firstly, the HWCB captures low-frequency information of the image in the shallow layers of the network, allowing for detailed exploration of subtle changes in the boundaries of coronary arteries. Secondly, the ResMamba module establishes long-range dependencies between features in the deep layers of the encoder and at the beginning of the decoder, improving the continuity of the segmentation process. Finally, the AFA module in the decoder reduces semantic differences between the encoder and decoder, which can capture small-slender coronary artery branches and further improve segmentation accuracy.<i>Main results.</i>Experiments on three coronary artery segmentation datasets have shown that the HWA-ResMamba outperforms other state-of-the-art methods in performance and generalization. Specifically, in the self-built dataset, HWA-ResMamba obtained Dice of 0.8857 and Hausdorff Distance (HD) of 1.9028, outperforming nnUnet by 0.0521, and 0.5489, respectively. HWA-ResMamba obtained Dice of 0.8371, and 0.7861 in the two public datasets, outperforming nnUnet by 0.0255, and 0.0107, respectively.<i>Significance.</i>Our method can accurately segment coronary arteries and can contribute to improved diagnosis and assessment of CAD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630838","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":"Editorial: Advances in online and real-time adaptive radiotherapy.","authors":"F Albertini, A McWilliam, B Winey","doi":"10.1088/1361-6560/adc183","DOIUrl":"https://doi.org/10.1088/1361-6560/adc183","url":null,"abstract":"","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701223","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}