Physics in medicine and biology最新文献

{"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":"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}

{"title":"Ultra-high energy spectral prompt PET.","authors":"Satyajit Ghosh, Valerio Cosmi, Ruud M Ramakers, Freek J Beekman, Marlies C Goorden","doi":"10.1088/1361-6560/adbfd7","DOIUrl":"10.1088/1361-6560/adbfd7","url":null,"abstract":"<p><p><i>Objective.</i>Utilizing prompt gammas in preclinical pinhole-collimated positron emission tomography (PET) avoids image degradation due to positron range blurring and photon down scatter, enables multi-isotope PET and can improve counting statistics for low-abundance positron emitters. This was earlier reported for¹²⁴I,⁸⁹Zr and simultaneous¹²⁴I -¹⁸F PET using the VECTor scanner (MILabs, The Netherlands), demonstrating sub-mm resolution despite long positron ranges. The aim of the present study is to investigate if such sub-mm PET imaging is also feasible for a large variety of other isotopes including those with extremely high energy prompt gammas (>1 MeV) or with complex emission spectra of prompt gammas.<i>Approach.</i>We use Monte Carlo simulations to assess achievable image resolutions and uniformity across a broad range of spectrum types and emitted prompt gamma energies (603 keV-2.2 MeV), using⁵²Mn,⁹⁴Tc,⁸⁹Zr,⁴⁴Sc,⁸⁶Y,⁷²As,¹²⁴I,³⁸K, and⁶⁶Ga.<i>Main results.</i>Our results indicate that sub-millimeter resolution imaging may be feasible for almost all isotopes investigated, with the currently used cluster pinhole collimators. At prompt gamma energies of 603 keV of¹²⁴I, an image resolution of ∼0.65 mm was achieved, while for emissions at 703, 744, 834, and 909 keV of⁹⁴Tc,⁵²Mn,⁷²As, and⁸⁹Zr, respectively, ∼0.7 mm resolution was obtained. Finally, at ultra-high energies of 1.2 (⁴⁴Sc) and 1.4 MeV (⁵²Mn) resolutions of ∼0.75 mm and ∼0.8 mm could still be achieved although ring artifacts were observed at the highest energies (1.4 MeV). For³⁸K (2.2 MeV), an image resolution of 1.2 mm was achieved utilizing its 2.2 MeV prompt emission.<i>Significance.</i>This work shows that current cluster pinhole collimators are suitable for sub-mm resolution prompt PET up till at least 1.4 MeV. This may open up new avenues to developing new tracer applications and therapies utilizing these PET isotopes.</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":"143616775","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":"Optimisation of magnetic field sensing with optically pumped magnetometers for magnetic detection electrical impedance tomography.","authors":"Kai Mason, Florencia Maurino-Alperovich, Kirill Aristovich, David Holder","doi":"10.1088/1361-6560/adc0df","DOIUrl":"10.1088/1361-6560/adc0df","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic detection electrical impedance tomography (MDEIT) is a novel technique that could enable non-invasive imaging of fast neural activity in the brain. However, commercial magnetometers are not suited to its technical requirements. The purpose of this work was to optimise the number, orientation and size of optically pumped magnetometers (OPMs) for MDEIT and inform the future development of MDEIT-specific magnetometers.<i>Approach.</i>Computational modelling was used to perform forward and inverse MDEIT modelling. Images were reconstructed using three sensing axes, arrays of 16 to 160 magnetometers, and cell sizes ranging from 1 to 18 mm. Image quality was evaluated visually and with the weighted spatial variance.<i>Main results.</i>Single-axis measurements normal to the surface provided the best image quality, and image quality increased with an increase in sensor number and size. The optimal sensing arrangement balancing image quality and practical implementation was measurement normal to the surface of the scalp using between 48 and 96 magnetometers with a cubic cell with an 18 mm side length.<i>Significance.</i>This study can inform future OPM design, showing the size of the vapour cell need not be constrained to that of commercially available OPMs, and that the development of a small array of single-axis, highly sensitive, high-bandwidth OPMs should be prioritised for fast neural MDEIT.</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":"143630840","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":"Development and dosimetric evaluation of a modulated intraoperative radiotherapy (mIORT) system using the Zeiss intrabeam device.","authors":"Xavier Jones, Gabor Neveri, Marsha Chin, Pejman Rowshanfarzad","doi":"10.1088/1361-6560/adc06f","DOIUrl":"10.1088/1361-6560/adc06f","url":null,"abstract":"<p><p><i>Objective.</i>Intraoperative radiotherapy (IORT) is a specialised radiotherapy technique that delivers a precise, single high-dose fraction to the tumour bed after surgical removal of the tumour, aiming to eliminate residual cancer cells. This study investigates the incorporation of novel applicators into an existing IORT system to enable dose modulation, performing Monte Carlo (MC) simulations, 3D printing, and experimental validation. The Zeiss Intrabeam IORT device, a low-kV IORT system capable of delivering x-rays nearly isotropically, with energies up to 50 kV, was used in this study.<i>Approach.</i>Applicators were modified to alter dose distributions, incorporating features such as shielding or changes to an ellipsoid shape. The EGSnrc MC code was employed to simulate the dose distributions of each applicator design, generating data such as dose maps, percentage depth dose (PDD) curves, per cent difference maps between shielded and unshielded regions, and energy spectra to characterise each applicator. Gafchromic EBT3 film measurements were performed on select 3D printed applicators, to verify the MC simulations, with dose distribution data extracted for comparison.<i>Main Results.</i>Visual comparisons of dose and percentage different maps indicate a high correlation between the MC simulations and film measurements. Most PDD points for spherical applicators showed deviations within 4%, while ellipsoid applicators had deviations of 14% for the unshielded and 5% for the shielded applicators. All Root Mean Square Error (RMSEs) were below 0.05 for spherical and 0.18 for ellipsoid designs. Based on film data, shielded ellipsoid applicators reduced the dose by ∼99%, 48%, 22%, and 8% at 0.3, 1, 2, and 3 cm, respectively, while shielded spherical applicators achieved ∼83%, 35%, 14%, and 7% reductions at the same distances. Energy spectra for photons exiting shielded regions were also generated.<i>Significance.</i>Results of this study may be used in the development of patient-specific IORT techniques, or the development of a treatment planning system involving mIORT.</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":"143625260","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":"Regularized origin ensemble with a beam prior for range verification in particle therapy with Compton-camera data.","authors":"Jona Kasprzak, Jorge Roser, Julius Werner, Nadja Kohlhase, Andreas Bolke, Lisa-Marie Kaufmann, Magdalena Rafecas","doi":"10.1088/1361-6560/adbfd8","DOIUrl":"10.1088/1361-6560/adbfd8","url":null,"abstract":"<p><p><i>Objective</i>. In particle therapy (PT), several methods are being investigated to help reduce range margins and identify deviations from the original treatment plan, such as prompt-gamma imaging with Compton cameras (CC). To reconstruct the images, the Origin Ensemble (OE) algorithm is commonly used. In the context of PT, artifacts and strong noise often affect CC images. To improve the ability of OE to identify range shifts, and also to enhance image quality, we propose to regularize OE using beam a-priori knowledge (<i>beam prior</i>).<i>Approach</i>. We implemented the beam prior to OE using the class of Gibbs' distribution functions. For evaluation, Monte-Carlo simulations of centered and off-center beams with therapeutic energies impinging on a PMMA target were conducted in GATE. To introduce range shifts, air layers were introduced into the target. In addition, the effect of a bone layer, closer to a realistic scenario, was investigated. OE with the beam prior (BP-OE) and conventional OE (reference) were compared using the spill-over-ratio (SOR) as well as shifts in the distal falloff in projections using cubic splines with Chebyshev nodes.<i>Main results</i>. BP-OE improved the shift estimates by up to 11% compared to conventional OE for centered and up to 250% with off-centered beams. BP-OE decreased the image noise level, improving the SOR significantly by up to 96%.<i>Significance</i>. BP-OE applied to CC data can improve shift estimations compared to conventional OE. The developed Gibbs-based regularization framework also allows further prior functions to be included into OE, for instance, smoothing or edge-preserving priors. BP-OE could be extended to PET-based range verification or multiple-beam scenarios.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616773","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}