Physics in medicine and biology最新文献

{"title":"Multi-task learning for automated contouring and dose prediction in radiotherapy.","authors":"Sangwook Kim, Aly Khalifa, Thomas G Purdie, Chris McIntosh","doi":"10.1088/1361-6560/adb23d","DOIUrl":"10.1088/1361-6560/adb23d","url":null,"abstract":"<p><p><i>Objective</i>. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in DL, the contouring and dose prediction tasks for automated treatment planning are done independently.<i>Approach</i>. In this study, we applied the multi-task learning (MTL) approach in order to seamlessly integrate automated contouring and voxel-based dose prediction tasks, as MTL can leverage common information between the two tasks and be able to increase the efficiency of the automated tasks. We developed our MTL framework using the two datasets: in-house prostate cancer dataset and the publicly available head and neck cancer dataset, OpenKBP.<i>Main results</i>. Compared to the sequential DL contouring and treatment planning tasks, our proposed method using MTL improved the mean absolute difference of dose volume histogram metrics of prostate and head and neck sites by 19.82% and 16.33%, respectively. Our MTL model for automated contouring and dose prediction tasks demonstrated enhanced dose prediction performance while maintaining or sometimes even improving the contouring accuracy. Compared to the baseline automated contouring model with the Dice score coefficients of 0.818 for prostate and 0.674 for head and neck datasets, our MTL approach achieved average scores of 0.824 and 0.716 for these datasets, respectively.<i>Significance</i>. Our study highlights the potential of the proposed automated contouring and planning using MTL to support the development of efficient and accurate automated treatment planning for radiotherapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189698","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":"Detection and correction of translational motion in SPECT with exponential data consistency conditions.","authors":"My Hoang Hoa Bui, Antoine Robert, Ane Etxebeste, Simon Rit","doi":"10.1088/1361-6560/adb09a","DOIUrl":"10.1088/1361-6560/adb09a","url":null,"abstract":"<p><p><i>Objective.</i>Rigid patient motion can cause artifacts in single photon emission computed tomography (SPECT) images, compromising the diagnosis and treatment planning. Exponential data consistency conditions (eDCCs) are mathematical equations describing the redundancy of exponential SPECT measurements. It has been recently shown that eDCCs can be used to detect patient motion in SPECT projections. This study aimed at developing a fully data-driven method based on eDCCs to estimate and correct for translational motion in SPECT.<i>Approach.</i>If all activity is encompassed inside a convex region<i>K</i>of constant attenuation, eDCCs can be derived from SPECT projections and can be used to verify the pairwise consistency of these projections. Our method assumes a single patient translation between two detector gantry positions. The proposed method estimates both the three-dimensional shift and the motion index, i.e. the index of the first gantry position after motion occurred. The estimation minimizes the eDCCs between the subset of projections before the motion index and the subset of motion-corrected projections after the motion index.<i>Results.</i>We evaluated the proposed method using Monte Carlo simulated and experimental data of a NEMA IEC phantom and simulated projections of a liver patient. The method's robustness was assessed by applying various motion vectors and motion indices. Motion detection and correction with eDCCs were sensitive to movements above 3 mm. The accuracy of the estimation was below the 2.39 mm pixel spacing with good precision in all studied cases. The proposed method led to a significant improvement in the quality of reconstructed SPECT images. The activity recovery coefficient relative to the SPECT image without motion was above 90% on average over the six spheres of the NEMA IEC phantom and 97% for the liver patient. For example, for a(2,2,2)cm translation in the middle of the liver acquisition, the activity recovery coefficient was improved from 35% (non-corrected projections) to 99% (motion-corrected projections).<i>Significance.</i>The study proposed and demonstrated the good performance of translational motion detection and correction with eDCCs in SPECT acquisition data.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143067296","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":"Multi-label segmentation of carpal bones in MRI using expansion transfer learning.","authors":"Stefan Raith, Matthias Deitermann, Tobias Pankert, Jianzhang Li, Ali Modabber, Frank Hölzle, Frank Hildebrand, Jörg Eschweiler","doi":"10.1088/1361-6560/adabae","DOIUrl":"10.1088/1361-6560/adabae","url":null,"abstract":"<p><p><i>Objective.</i>The purpose of this study was to develop a robust deep learning approach trained with a small<i>in-vivo</i>MRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis.<i>Approach.</i>A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study. The MRI data was variable with respect to the field of view (FOV), wide range of image intensity, and joint pose. A<i>two-stage</i>segmentation pipeline using modified 3D U-Net was proposed. In the<i>first stage</i>, a novel architecture, introduced as expansion transfer learning (ETL), cascades the use of a focused region of interest (ROI) cropped around ground truth for pretraining and a subsequent transfer by an expansion to the original FOV for a primary prediction. The bounding box around the ROI generated was utilized in the<i>second stage</i>for high-accuracy, labeled segmentations of eight carpal bones. Different metrics including dice similarity coefficient (DSC), average surface distance (ASD) and hausdorff distance (HD) were used to evaluate performance between proposed and four state-of-the-art approaches.<i>Main results.</i>With an average DSC of 87.8 %, an ASD of 0.46 mm, an average HD of 2.42 mm in all datasets (96.1 %, 0.16 mm, 1.38 mm in 12 datasets after exclusion criteria, respectively), the proposed approach showed an overall strongest performance than comparisons.<i>Significance.</i>To our best knowledge, this is the first CNN-based multi-label segmentation approach for MRI human carpal bones. The ETL introduced in this work improved the ability to localize a small ROI in a large FOV. Overall, the interplay of a<i>two-stage</i>approach and ETL culminated in convincingly accurate segmentation scores despite a very small amount of image data.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010003","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":"A ConvLSTM-based model for predicting thermal damage during laser interstitial thermal therapy.","authors":"Tingting Gao, Libin Liang, Hui Ding, Chao Zhang, Xiu Wang, Wenhan Hu, Kai Zhang, Guangzhi Wang","doi":"10.1088/1361-6560/adb3ea","DOIUrl":"10.1088/1361-6560/adb3ea","url":null,"abstract":"<p><p><i>Objective.</i>Accurate prediction of thermal damage extent is essential for effective and precise thermal therapy, especially in brain laser interstitial thermal therapy (LITT). Immediate postoperative contrast-enhanced T1-weighted imaging (CE-T1WI) is the primary method for clinically assessing<i>in vivo</i>thermal damage after image-guided LITT. CE-T1WI reveals a hyperintense enhancing rim surrounding the target lesion, which serves as a key radiological marker for evaluating the thermal damage extent. Although widely used in clinical practice, traditional thermal damage models rely on empirical parameters from<i>in vitro</i>experiments, which can lead to inaccurate predictions of thermal damage<i>in vivo</i>. Additionally, these models predict only two tissue states (damaged or undamaged), failing to capture three tissue states observed on post-CE-T1WI images, highlighting the need for improved thermal damage prediction methods.<i>Approach.</i>This study proposes a novel convolutional long short-term memory-based model that utilizes intraoperative temperature distribution history data measured by magnetic resonance temperature imaging (MRTI) during LITT to predict the enhancing rim on post-CE-T1WI images. This method was implemented and evaluated on retrospective data from 56 patients underwent brain LITT.<i>Main results.</i>The proposed model effectively predicts the enhancing rim on postoperative images, achieving an average dice similarity coefficient of 0.82 (±0.063) on the test dataset. Furthermore, it generates real-time predicted thermal damage area variation trends that closely resemble those of the traditional thermal damage model, suggesting potential for real-time prediction of thermal damage extent.<i>Significance.</i>This method could provide a valuable tool for visualizing and assessing intraoperative thermal damage extent.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370935","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":"Transition to GPU-based reconstruction for clinical organ-targeted PET scanner.","authors":"Borys Komarov, Henry Maa-Hacquoil, Harutyun Poladyan, Brandon Baldassi, Anirudh Shahi, Edward Anashkin, Oleksandr Bubon, Alla Reznik","doi":"10.1088/1361-6560/adb198","DOIUrl":"10.1088/1361-6560/adb198","url":null,"abstract":"<p><p><i>Objective.</i>This article explores a new graphics processing unit (GPU)-based techniques for efficient image reconstruction in organ-targeted positron emission tomography (PET) scanners with planar detectors.<i>Approach.</i>GPU-based reconstruction is applied to the Radialis low-dose organ-targeted PET technology, developed to overcome the issues of high exposure and limited spatial resolution inherent in traditional whole-body PET/CT (Computed Tomography) scans. The Radialis planar detector technology is based on four-side tileable sensor modules that can be seamlessly combined into a sensing area of the needed size, optimizing the axial field-of-view for specific organs, and maximizing geometric sensitivity. The article explores the transition from central processing unit-based maximum likelihood expectation maximization algorithms to a GPU-based counterpart, demonstrating a tenfold overall speedup in image reconstruction with a hundredfold improvement in iteration speed.<i>Main results.</i>Through standardized PET performance tests and clinical image analysis, this work demonstrates that GPU-based image reconstruction maintains diagnostic image quality while significantly reducing reconstruction times. The application of this technology, particularly in breast imaging using the Radialis low-dose positron emission mammography, significantly reduces exam times thus improving patient comfort and throughput in clinical settings.<i>Significance.</i>This study represents an important advancement in the clinical workflow of PET imaging, providing insights into optimizing reconstruction algorithms to effectively leverage the parallel processing capabilities of GPUs.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123241","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 TMS dosimetry: evaluating the effective electric field as a novel metric.","authors":"Micol Colella, Micaela Liberti, Filippo Carducci, Giorgio Leodori, Giacomo Maria Russo, Francesca Apollonio, Alessandra Paffi","doi":"10.1088/1361-6560/adae4b","DOIUrl":"10.1088/1361-6560/adae4b","url":null,"abstract":"<p><p><i>Objective</i>. This study introduces the effective electric field (<i>E</i>_eff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry.<i>E</i>_effrepresents the electric field component aligned with the local orientation of cortical and white matter (WM) neuronal elements. To assess the utility of<i>E</i>_effas a predictive measure for TMS outcomes, we evaluated its correlation with TMS induced muscle responses and compared it against conventional observables, including the electric (<i>E</i>-)field magnitude, and its components normal and tangential to the cortical surface.<i>Approach.</i>Using a custom-made software for TMS dosimetry, the<i>E</i>_effis calculated combining TMS dosimetric results from an anisotropic head model with tractography data of gray and white matter (GM and WM). To test the hypothesis that<i>E</i>_effhas a stronger correlation with muscle response, a proof-of-concept experiment was conducted. Seven TMS sessions, with different coil rotations, targeted the primary motor area of a healthy subject. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle.<i>Main results.</i>The<i>E</i>_efftrend for the seven TMS coil rotations closely matched the measured MEP response, displaying an ascending pattern that peaked and then symmetrically declined. In contrast, the<i>E</i>-field magnitude and its components tangential (<i>E</i>_tan) and normal (<i>E</i>_norm) to the cortical surface were less responsive to coil orientation changes.<i>E</i>_effshowed a strong correlation with MEPs (<i>r</i>= 0.8), while the other observables had a weaker correlation (0.5 for<i>E</i>_normand below 0.2 for<i>E</i>-field magnitude and<i>E</i>_tan).<i>Significance.</i>This study is the first to evaluate<i>E</i>_eff, a novel component of the TMS induced<i>E</i>-field. Derived using tractography data from both white and GM,<i>E</i>_effinherently captures axonal organization and local orientation. By demonstrating its correlation with MEPs, this work introduces<i>E</i>_effas a promising observable for future TMS dosimetric studies, with the potential to improve the precision of TMS applications.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143041080","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":"MLAR-UNet: LDCT image denoising based on U-Net with multiple lightweight attention-based modules and residual reinforcement.","authors":"Hao Tang, Ningfeng Que, Yanwen Tian, Mingzhe Li, Alessandro Perelli, Yueyang Teng","doi":"10.1088/1361-6560/adb19a","DOIUrl":"10.1088/1361-6560/adb19a","url":null,"abstract":"<p><p><i>Objective.</i>Computed tomography (CT) is a crucial medical imaging technique which uses x-ray radiation to identify cancer tissues. Since radiation poses a significant health risk, low dose acquisition procedures need to be adopted. However, low-dose CT (LDCT) can cause higher noise and artifacts which massively degrade the diagnosis.<i>Approach.</i>To denoise LDCT images more effectively, this paper proposes a deep learning method based on U-Net with multiple lightweight attention-based modules and residual reinforcement (MLAR-UNet). We integrate a U-Net architecture with several advanced modules, including Convolutional Block Attention Module (CBAM), Cross Residual Module (CR), Attention Cross Reinforcement Module (ACRM), and Convolution and Transformer Cross Attention Module (CTCAM). Among these modules, CBAM applies channel and spatial attention mechanisms to enhance local feature representation. However, serious detail loss caused by incorrect embedding of CBAM for LDCT denoising is verified in this study. To relieve this, we introduce CR to reduce information loss in deeper layers, preserving features more effectively. To address the excessive local attention of CBAM, we design ACRM, which incorporates Transformer to adjust the attention weights. Furthermore, we design CTCAM, which leverages a complex combination of Transformer and convolution to capture multi-scale information and compute more accurate attention weights.<i>Results.</i>Experiments verify the embedding rationality and validity of each module and show that the proposed MLAR-UNet denoises LDCT images more effectively and preserves more details than many state-of-the-art methods on clinical chest and abdominal CT datasets.<i>Significance.</i>The proposed MLAR-UNet not only demonstrates superior LDCT image denoising capability but also highlights the strong detail comprehension and negligible overheads of our designed ACRM and CTCAM. These findings provide a novel approach to integrating Transformer more efficiently in image processing.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123240","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":"Study-level cross-modal retrieval of chest x-ray images and reports with adapter-based fine-tuning.","authors":"Yingjie Chen, Weihua Ou, Zhifan Gao, Lingge Lai, Yang Wu, Qianqian Chen","doi":"10.1088/1361-6560/adaf05","DOIUrl":"https://doi.org/10.1088/1361-6560/adaf05","url":null,"abstract":"<p><p>Cross-modal retrieval is crucial for improving clinical decision-making and report generation. However, current technologies mainly focus on linking single images with reports, ignoring the need to comprehensively observe multiple images in real clinical environments. Additionally, differences in imaging equipment, scanning parameters, geographic regions, and reporting styles in chest x-rays and reports cause inconsistent data distributions, which challenge model reliability and generalization. To address these challenges, we propose a study-level cross-modal retrieval task for chest x-rays and reports to better meet clinical needs. Our study-level approach involves cross-modal retrieval between multiple images and reports from patient exams. Given a set of study-level images or reports, our method retrieves relevant reports or images from a database, providing a more realistic reflection of clinical scenarios compared to traditional methods that link single images with reports. Furthermore, we introduce an adapter-based pre-training and fine-tuning method to enhance model generalization across diverse data distributions. Through comprehensive experiments, we demonstrate the advantages of our method in pre-training and fine-tuning. In the pre-training stage, we compare our method with the latest techniques, showing the effectiveness of integrating study-level image features using a vision transformer and aligning them with report features. In the fine-tuning stage, we compare the adapter-based fine-tuning method with the latest methods of full-parameter fine-tuning and conduct ablation studies with common head-based and full-parameter fine-tuning methods, proving our method's efficiency and significant potential for practical clinical applications. This study proposes a study-level cross-modal retrieval task for matching chest x-ray images and reports. By employing a pre-training and fine-tuning strategy with adapter modules, it addresses the issue of data distribution inconsistency and improves retrieval performance.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 4","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143409992","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":"High-temporal-resolution dynamic PET imaging based on a kinetic-induced voxel filter.","authors":"Liwen Fu, Zixiang Chen, Yanhua Duan, Zhaoping Cheng, Lingxin Chen, Yongfeng Yang, Hairong Zheng, Dong Liang, Zhi-Feng Pang, Zhanli Hu","doi":"10.1088/1361-6560/adae4e","DOIUrl":"https://doi.org/10.1088/1361-6560/adae4e","url":null,"abstract":"<p><p><i>Objective</i>. Dynamic positron emission tomography (dPET) is an important molecular imaging technology that is used for the clinical diagnosis, staging, and treatment of various human cancers. Higher temporal imaging resolutions are desired for the early stages of radioactive tracer metabolism. However, images reconstructed from raw data with shorter frame durations have lower image signal-to-noise ratios (SNRs) and unexpected spatial resolutions.<i>Approach</i>. To address these issues, this paper proposes a kinetic-induced voxel filtering technique for processing noisy and distorted dPET images. This method extracts the inherent motion information contained in the target PET image and effectively uses this information to construct an image filter for each PET image frame. To ensure that the filtered image remains undistorted, we integrate and reorganize the information from each frame along the temporal dimension. In addition, our method applies repeated filtering operations to the image to produce optimal denoising results.<i>Main results</i>. The effectiveness of the proposed method is validated on both simulated and clinical dPET data, with quantitative evaluations of dynamic images and pharmacokinetic parameter maps calculated via the peak SNR and mean structural similarity index measure. Compared with the state-of-the-art methods, our method achieves superior results in both qualitative and quantitative imaging scenarios.<i>Significance</i>. It exhibits commendable performance and high interpretability and is demonstrated to be both effective and feasible in high-temporal-resolution dynamic PET imaging tasks.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 4","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143409957","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":"Proton minibeam (pMBRT) radiation therapy: experimental validation of Monte Carlo dose calculation in the RayStation TPS.","authors":"Yuting Lin, Erik Traneus, Aoxiang Wang, Wangyao Li, Hao Gao","doi":"10.1088/1361-6560/adae4f","DOIUrl":"10.1088/1361-6560/adae4f","url":null,"abstract":"<p><p><i>Background.</i>Proton minibeam radiation therapy (pMBRT) is a spatially fractionated radiation therapy modality that uses a multi-slit collimator (MSC) to create submillimeter slit openings for spatial dose modulation. The pMBRT dose profile is characterized by highly heterogeneous dose in the plane perpendicular to the beam and rapidly changing depth dose profiles. Dose measurements are typically benchmarked against in-house Monte Carlo (MC) simulation tools. For preclinical and clinical translation, a treatment planning system (TPS) capable of accurately predicting pMBRT doses in tissue and accessible on a commercial platform is essential. This study focuses on the beam modeling and verification of pMBRT using the RayStation TPS, a critical step in advancing its clinical implementation.<i>Methods.</i>The pMBRT system was implemented in RayStation for the IBA Proteus®ONE single-room compact proton machine. The RayStation pMBRT model is an extension of the clinical beam model, allowing pMBRT dose calculations through the MSC using the existing clinical beam model. Adjustable MSC parameters include air gap, slit thickness, slit pitch, number of slits, slits direction and slit thickness. The pMBRT TPS was validated experimentally against measurements using six different collimators with various slit widths (0.4-1.4 mm) and center-to-center slit distances (2.8-4.0 mm). Each collimator comprised five non-divergent slits. Validation involved MatriXX measurements for average dose, Gafchromic film placed at varying depths to measure lateral dose profiles, and film placed along the beam axis to measure depth-dose curves in solid water phantoms. A single 150 MeV energy layer with a 0.5 cm spot spacing was used to create a uniform radiation map across the MSC field.<i>Results.</i>The comparison of average depth dose measurements with RayStation MC calculations showed a gamma passing rate better than 95% using 3 mm/3% criteria, except for the 0.4 mm slit width. After adjusting the slit width by 40-60<i>μ</i>m to account for machining uncertainties, the gamma passing rate exceeded 95% under the same criteria. For the peaks and valleys of the percentage depth doses, agreement between RayStation and film measurements was above 90% using 2 mm/5% criteria, except in the high linear energy transfer region. Lateral profile comparisons at depths of 2, 6, and 10 cm demonstrated over 90% agreement for all curves using 0.2 mm/5% criteria.<i>Conclusions.</i>The pMBRT beam model for the Proteus®ONE-based system has been successfully implemented in RayStation TPS, with its initial accuracy validated experimentally. Further measurements, including additional energies and Spread Out Bragg Peaks, are required to complete the clinical commissioning process.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143041086","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}