Medical physics最新文献

{"title":"Mini-lattice radiation therapy: A treatment planning approach to miniaturize spatially fractionated lattice radiation therapy using a clinical linear accelerator","authors":"Daiki Hara, Houssam Abou-Mourad, John A. Antolak, Jack C. Thull, Nadia N. Laack, Chelsea Self, Alfredo Fernandez-Rodriguez, Yolanda Prezado, Hok Seum W. C. Tseung, William G. Breen, Scott C. Lester, Robert W. Mutter, Sean S. Park, Michael P. Grams","doi":"10.1002/mp.70027","DOIUrl":"10.1002/mp.70027","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Spatially fractionated radiation therapy (SFRT) is a technique that delivers heterogenous dose distributions consisting of alternating regions of high dose “peaks” and low dose “valleys”. Current delivery methods for SFRT using megavoltage x-rays usually treat large and bulky tumors with brass grid or volumetric modulated arc therapy (VMAT) lattice techniques. The size and spacing of high dose regions in these approaches are typically on the order of centimeters. However, multiple studies have suggested that decreasing these dimensions may improve the therapeutic ratio. Furthermore, a more compact approach to SFRT would allow for a greater number of high dose regions within the tumor, as well as application to smaller and more irregularly shaped targets thereby increasing the number of patients that could benefit from SFRT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study describes the commissioning and first patient treatment using mini-lattice radiation therapy (MLRT). MLRT uses a clinical linear accelerator and decreases the size and spacing of standard lattice SFRT by using individual multileaf collimator (MLC) leaves to deliver 5 mm wide high dose regions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>MLRT plans were created in the Varian Eclipse treatment planning system for a Varian Truebeam equipped with Millennium 120 MLCs. MLRT uses 6 MV Flattening Filter Free high dose rate and the width of individual MLCs to define 5 mm by 5 mm openings separated by closed MLCs to deliver alternating opened and blocked regions. Dynamic conformal arcs were used to conform MLCs to 4 mm spherical mini-lattice structures in the gross tumor volume (GTV). A MLRT-specific beam model was commissioned to accurately model the small MLRT fields. Film measurements were performed to assess the accuracy of MLRT plans calculations. Plans for seven treatment sites in different parts of the body for retrospective patient candidates were created with varying numbers of mini-lattices and separation distances to assess the impact of varying these parameters on treatment dose metrics. MLRT was used for the first time to treat a patient with two fractions of MLRT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The AcurosXB calculation algorithm with modified x and y spot sizes, dosimetric leaf gap, transmission factor, and output factor table was used to generate a beam model for accurate MLRT calculations. The MLRT-specific beam model resulted in gamma passing rates (1%/0.5 mm criteria) of 90%–99% for retrospective patient MLRT film measurements. Dose volume histogram statistics, equivalent uniform dose, and m","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145188188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel adaptive energy switching algorithm for proton arc therapy based on the machine-specific delivery characteristics","authors":"Yujia Qian,&nbsp;Riao Dao,&nbsp;Lewei Zhao,&nbsp;Shiyi Zhou,&nbsp;Qingkun Fan,&nbsp;Guillaume Janssens,&nbsp;Bas A. de Jong,&nbsp;Stefan Both,&nbsp;Erik Korevaar,&nbsp;Ting Hu,&nbsp;Gang Peng,&nbsp;Zhiyong Yang,&nbsp;Sheng Zhang,&nbsp;FangFang Yin,&nbsp;Manju Liu,&nbsp;Kunyu Yang,&nbsp;Hong Quan,&nbsp;Xuanfeng Ding,&nbsp;Gang Liu","doi":"10.1002/mp.70011","DOIUrl":"10.1002/mp.70011","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) is treatment delivery efficiency. Previous studies focus on reducing the number of energy layers by ascending switching to shorten the beam delivery time. However, this is not true of all proton therapy systems. The new energy layer switching system was recently upgraded in the University Medical Center Groningen (UMCG), which enables a fast energy layer ascending switching (ELAS).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc-_AES) based on the machine-specific delivery characteristics of proton therapy systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The SPArc-_AES optimization algorithm is based on the polynomial increasing feature of energy layer ascending switching. <i>K</i>-Medoids clustering analysis and simulated annealing algorithm were used to optimize the energy delivery sequence. Ten cases were selected to evaluate the plan quality, plan robustness, and the delivery efficiency compared with the previously SPArc energy sequence optimization algorithm, SPArc_seq.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Without extra constraints in the energy ascending constraints, the SPArc-_AES offers a better plan quality and robustness, while the treatment delivery efficiency was significantly improved compared to the SPArc_seq. More specifically, SPArc-_AES effectively shortened the energy layer switching time and the beam delivery time by 34.03% and 31.10%, respectively, while offering better target dose conformality and generally lower dose to organs-at-risk.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Based on the machine-specific delivery characteristics, we introduced a novel adaptive energy switching algorithm for efficient SPArc optimization, which could significantly improve delivery efficiency while enhancing the plan quality by eliminating no longer necessary constraints on the total number of energy layer ascending switching.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145188121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of water-equivalent plastic scintillator doped with zirconium oxide nanoparticles","authors":"Xue Gang Chu(褚薛刚),&nbsp;Bao Guo Zhang(张保国),&nbsp;Jun Hui Wang(王君辉),&nbsp;Yong Li(李泳)","doi":"10.1002/mp.70048","DOIUrl":"https://doi.org/10.1002/mp.70048","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Scintillator-based detectors can provide real-time measurement of small fields with high dose gradients and have the advantages of good repeatability, linear response, and excellent spatial resolution. For radiotherapy dose measurement, water—equivalency of the scintillator can be beneficial based on current clinical standards. It would ideally match water in effective atomic number, electron density, and mass density.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Plastic scintillators are primarily composed of hydrocarbon molecules. While their interaction with photons exhibits properties similar to water, they are incompletely equivalent. This study aimed to develop a water-equivalent plastic scintillator by doping the scintillators with a specific proportion of oxide nanoparticles. The nanoparticles must be less than 10 nm to maintain transparency.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Zirconium oxide (ZrO&lt;sub&gt;2&lt;/sub&gt;) nanoparticles smaller than 10 nm were synthesized, and their surface was modified using methacryloxy propyl trimethoxyl silane (MPS) to ensure good dispersibility. The precise elemental composition of the modified ZrO&lt;sub&gt;2&lt;/sub&gt; nanoparticles was determined using inductively coupled plasma (ICP) analysis to develop water-equivalent plastic scintillators. The initial doping ratio of MPS-ZrO&lt;sub&gt;2&lt;/sub&gt; in a water-equivalent plastic scintillator was estimated using an empirical formula. Meanwhile, the precise doping ratio of MPS-ZrO&lt;sub&gt;2&lt;/sub&gt; in water equivalent plastic scintillator was determined through a simulation performed with the Monte Carlo (MC) program GEANT4. Finally, the water-equivalent plastic scintillator was synthesized by in situ polymerization, and its water equivalence and dosimetric performance were validated using experimental tests.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Transmission electron microscope imaging indicated that the newly prepared MPS-ZrO&lt;sub&gt;2&lt;/sub&gt; nanoparticles exhibited a size of approximately 4–5 nm, with uniform distribution and no aggregation. The ICP analysis determined ZrO&lt;sub&gt;2&lt;/sub&gt; and MPS contents in the nanoparticles to be 67.47% and 31.82%, respectively. Based on the empirical formula and MC simulation, the optimal doping concentration of MPS-ZrO&lt;sub&gt;2&lt;/sub&gt; nanoparticles in the water-equivalent plastic scintillator was 0.53 wt%. The physical density of the synthesized plastic scintillator was measured at 1.049 ± 0.127 g/cm&lt;sup&gt;3&lt;/sup&gt;, with an electron density of 3.536 × 10&lt;sup&gt;23&lt;/sup&gt; E/cm&lt;sup&gt;3&lt;/sup&gt;, closely matching that of water. The maximum deviation in x-ray attenuation between water and the plastic ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding time–activity curve and time-integrated activity variations in radiopharmaceutical therapy challenge: Experience and results","authors":"Oleksandra V. Ivashchenko,&nbsp;Jim O'Doherty,&nbsp;Deni Hardiansyah,&nbsp;Elisa Grassi,&nbsp;Johannes Tran-Gia,&nbsp;Johannes W. T. Heemskerk,&nbsp;Eero Hippeläinen,&nbsp;Mattias Sandström,&nbsp;Marta Cremonesi,&nbsp;Gerhard Glatting","doi":"10.1002/mp.70043","DOIUrl":"https://doi.org/10.1002/mp.70043","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The process of determining/calculating the time–activity curve (TAC) for radiopharmaceutical therapy (RPT) is generally heavily dependent on user- and site-dependent steps (e.g., the number and schedule of measurement points to be used, selection of the fit function), each having a notable effect on the determination of the time-integrated activity coefficient (TIAC) and thus on the calculated absorbed dose. Despite the high clinical importance of absorbed doses, there is no consensus on the methodology for processing time–activity data or even a clear understanding of the influence of various uncertainties and user-dependent variations in personalized RPT dosimetry on the accuracy of TAC calculations.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To address this critical unmet need, the &lt;b&gt;t&lt;/b&gt;ime–&lt;b&gt;a&lt;/b&gt;ctivity &lt;b&gt;c&lt;/b&gt;urve and &lt;b&gt;t&lt;/b&gt;ime-&lt;b&gt;i&lt;/b&gt;ntegrated activity variations (TACTIC) AAPM Grand Challenge was designed to explore the variations in TAC modeling for RPT applications.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Launched in January 2023, the TACTIC challenge consisted of three phases: i) warm-up phase (phase 0, to gain familiarity with the logistics and the modalities of the challenge), ii) TAC fitting based on data from individual patients (phase 1, rated to determine winner 1), and iii) TAC fitting using population-based data (phase 2, rated to determine winner 2). Based on the distributed synthetic biokinetic data of [&lt;sup&gt;177&lt;/sup&gt;Lu]Lu-PSMA-617 RPT (kidney, blood, and tumor), participants were asked to model the TAC and calculate the TIAC values for each of these tissues to the best of their ability. In addition, participants were requested to submit information about the fit function and fit optimization parameters. The best-performing team in each phase was determined on the basis of total root-mean-square error (RMSE) value across all three tissues.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total of 132 teams from over 30 countries registered for this data-driven challenge, of which 95 individual groups submitted their results throughout the challenge. By presenting participants with an identical set of measurement points previously generated from measured biokinetic data and providing additional a priori information about the procedure at different stages of the challenge, we could assess the degree of variation within the TIAC estimation. We investigated which of the commonly used TIAC estimation methods performs best and could therefore be used to harmonize TAC modeling in ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale nested graph transformer with graph operations: Advancing high-resolution chest x-ray classification","authors":"Dongjing Shan,&nbsp;Mengchu Yang,&nbsp;Lu Huang,&nbsp;Dawa Panduo,&nbsp;Biao Qu","doi":"10.1002/mp.70003","DOIUrl":"10.1002/mp.70003","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Accurate classification of high-resolution chest x-ray (CXR) images is critical for diagnosing lung conditions such as pneumonia and identifying small lesion targets, which demands precise feature extraction from multi-scale anatomical structures. Traditional deep learning models face challenges in balancing local detail retention and global context modeling, particularly with limited labeled data and high computational costs for high-resolution inputs.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study introduces a multi-scale nested graph transformer (MNGT) to address these challenges, aiming to enhance classification accuracy for high-resolution CXR images while improving computational efficiency and generalization in data-constrained scenarios.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;(1) Multi-scale nested architecture: High-resolution CXR images are segmented into hierarchical squares: first divided into large blocks, then further subdivided into smaller patches. A graph Transformer with variable attention scope processes these patches to capture local-to-global features, preserving fine details of small lesions (e.g., nodule contours) while modeling long-range dependencies (e.g., lung texture patterns); (2) Cross-Attention Fusion: Features from high-resolution and downscaled low-resolution images are fused using a cross-attention-based graph Transformer, enabling semantic interaction between scales and enhancing lesion discriminability; (3) Graph Pooling for Efficiency: Graph pooling aggregates patches into semantic regions, reducing token count and computational complexity (e.g., from 2401 to 196 tokens) while maintaining structural integrity; (4) Inductive Bias Integration: By incorporating graph convolution and adaptive receptive field adjustments, the model mitigates overfitting in small datasets, leveraging spatial prior knowledge to improve generalization.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Through extensive experiments on three types of high-resolution CXR images, we demonstrate the superiority of our architecture, surpassing other models in terms of both accuracy and F1-score. Furthermore, our ablation study highlights the efficiency of our designed architecture. The code including comparative models are publicly available on the Website: GitHub/MNGT.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;MNGT provides an efficient and robust solution for high-resolution CXR classification, combining local detail preservation, global context modeling, and inductive bias to excel i","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image-guided surgery: Technology and opportunities for medical physics","authors":"Jeffrey H. Siewerdsen","doi":"10.1002/mp.70049","DOIUrl":"https://doi.org/10.1002/mp.70049","url":null,"abstract":"<p>A brief review of technologies for image-guided surgery is offered, including tracking/navigation, intraoperative imaging, image registration, visualization, and surgical robotics across a spectrum of surgical applications. Opportunities for medical physicists to expand expertise and contribute more deeply within the circle of care in surgery are outlined, including improved safety, system integration/interoperability, and rigorous quality assurance.</p>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Method for end-user validation of deformable dose accumulation uncertainty modelling tools","authors":"John Kipritidis,&nbsp;Alexandra Quinn,&nbsp;Tomasz Morgas,&nbsp;Sven Kuckertz,&nbsp;Nils Papenberg,&nbsp;Stefan Heldmann,&nbsp;Nasim Givehchi,&nbsp;Thomas Coradi,&nbsp;Jeremy T. Booth","doi":"10.1002/mp.18094","DOIUrl":"https://doi.org/10.1002/mp.18094","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Deformable dose accumulation (DDA) uncertainty models can inform treatment decisions by communicating the dosimetric impact of deformable image registration (DIR) errors over multiple fractions. Currently there is limited guidance on how end-users can validate such models in the clinic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We propose an end-user validation sequence for DDA uncertainty modelling tools, akin to an acceptance test, using existing patient data and a clinical treatment planning system (TPS) as the evaluation platform.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed test sequence begins with a single “fixed” image (planning CT with associated contours and treatment plan) and a “moving” image (e.g., fractional synthetic CT with calculated dose) and uses the TPS to simulate multiple fractional DIRs as input to a DDA uncertainty model. Outputs of the uncertainty tool—including volumetric images of DIR spatial uncertainties, and associated uncertainties on propagated dose—are imported back to the TPS and a series of visual and semi-quantitative (point-based and DVH-based) cross-checks are carried out. Emphasis is placed on the use of standard dose and distance measurement tools, in conjunction with vendor-provided equations, to evaluate correctness of the uncertainty tool outputs at contour boundaries and in bulk tissue, considering variable DIR quality for both targets and organs at risk.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The test sequence is demonstrated for a non-clinical uncertainty tool using a clinical bladder case. Agreement within 2 voxels (for spatial uncertainties) and up to 5% of the prescribed dose (for dose uncertainties) is shown to be achievable for regions of plausible deformation and stable dose gradient (e.g., &lt; 5%/voxel).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>As the use of DDA in adaptive treatment becomes more commonplace, use of DDA uncertainty tools will become increasingly important to inform adaptive treatment decisions. This work represents an important effort to formalize an end-user validation process using standardly available clinical tools.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based deep learning for predicting brain tumor recurrence using magnetic resonance imaging","authors":"Qiuyu Zhou,&nbsp;Xuwei Tian,&nbsp;Meiling Feng,&nbsp;Lintao Li,&nbsp;Desheng Zheng,&nbsp;Xiaoyu Li","doi":"10.1002/mp.70016","DOIUrl":"10.1002/mp.70016","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Deep learning (DL) models, particularly those based on Transformer architecture, which are capable of capturing complex patterns and dependencies in medical imaging data, have shown great potential in improving brain tumor prognosis and guiding treatment decisions. However, the effectiveness of Transformer-based models, especially in predicting recurrence after treatment, has yet to be fully demonstrated.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aims to develop and validate a Transformer-based DL model that utilizes multi-modal data, specifically pre-treatment magnetic resonance imaging (MRI) scans fused with radiotherapy dose (RTDose) information, to predict post-treatment recurrence in brain tumors, thereby providing decision support for personalized radiotherapy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In this study, we employed MRI data from patients with brain metastases who had undergone Gamma Knife radiosurgery at the University of Mississippi Medical Center to train and validate a Transformer-based DL model. To further validate the Transformer-based model, a comparative analysis was conducted with nine established prognostic models. The generalizability and predictive accuracy of the model were validated across multiple clinical subgroups. To further exclude other potential factors influencing brain tumor recurrence, logistic regression (LR) and statistical analysis were conducted to confirm the independence of the model's predictions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The model achieved an average area under the receiver operating characteristic curve (AUROC) of 0.817 on 3-fold cross-validation, outperforming all other models. The model also exhibited strong generalizability across clinical subgroups, with AUROCs of 0.806 for patients under 50, 0.723 for those aged 51–60, and 0.843 for those aged 61–77 (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;0.057&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$p = 0.057$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). For gender subgroups, the AUROCs were 0.783 for females and 0.820 for males (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;0.057&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$p = 0.057$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). LR analysis confirmed the independence of the model's p","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploratory study on ultrasound image denoising using feature extraction and adversarial diffusion model","authors":"Yue Hu,&nbsp;Huiying Xu,&nbsp;Xinzhong Zhu,&nbsp;Xiao Huang","doi":"10.1002/mp.70023","DOIUrl":"10.1002/mp.70023","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In ultrasound imaging, the generated images involve speckle noise owing to the mechanism underlying image generation. Speckle noise directly affects image analysis, necessitating its effective suppression.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Ultrasound image denoising offers limited performance and causes structural information loss. To address these challenges and improve ultrasound image quality, we develop a new denoising method based on the diffusion model (DM).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This exploratory study proposes a DM-based denoising method, namely adversarial DM with feature extraction network (ADM-ExNet) to investigate the potential of combining diffusion models and generative adversarial Networks (GANs) for ultrasound image denoising. Specifically, we replace the reverse process of the DM with a GAN and modify the generator and discriminator as a U-Net structure. Simultaneously, a structural feature extraction network is incorporated into the model to construct a loss function, which offers enhanced detail retention. The noise levels (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;σ&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;15&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;20&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$sigma = 10, 15, 20$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) were simulated by adding Gaussian noise to the original ultrasound images, where &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;σ&lt;/mi&gt;\u0000 &lt;annotation&gt;$sigma$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; controls the intensity of the noise. We employed three public datasets, HC18, CAMUS, and Ultrasound Nerve, which involve the ultrasound images of the fetal head circumference, heart, and nerves, respectively. Each image was adjusted to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;256&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mn&gt;256&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$256times 256$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; pixels, and the training set and the validation set were divided by 9:1. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were employed as primary evaluation metrics. To rigorously validate the statistical significance of performance differences, we further applied false discovery rate (FDR) correction ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electron and proton FLASH beam dosimetry using unified alanine, EBT-XD, and HD-V2 Gafchromic film dosimeters","authors":"Seongmoon Jung,&nbsp;In Jung Kim,&nbsp;Chul-Young Yi,&nbsp;Yun Ho Kim,&nbsp;Young Min Seong,&nbsp;Rukundo Solomon,&nbsp;Sang Hyoun Choi,&nbsp;Young-jae Jang,&nbsp;Se Byeong Lee,&nbsp;Chae-Eon Kim,&nbsp;Sang-il Pak,&nbsp;Jong In Park","doi":"10.1002/mp.70022","DOIUrl":"https://doi.org/10.1002/mp.70022","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Ultra-high dose rate (UHDR) radiotherapy, or FLASH RT, has shown potential to spare normal tissues while maintaining tumor control. However, accurate dosimetry at UHDR remains challenging, as conventional ionization chambers suffer from recombination effects. Although radiochromic films and alanine dosimeters have both been investigated independently for FLASH dosimetry, their separate use hinders robust validation and direct comparison of their measurements.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aims to develop and evaluate a unified dosimeter containing both alanine and radiochromic film for electron and proton FLASH beam dosimetry. The design allows for simultaneous, co-located irradiation of both dosimeter types, enabling a direct comparison between them. This configuration eliminates confounding factors such as positional offsets, alignment errors, and beam fluctuations, thereby facilitating the validation of measurements and enhancing confidence in FLASH dosimetry.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The unified alanine and EBT-XD/HD-V2 film dosimeter was designed with the same outer dimensions as the Advanced Markus chamber (PTW-Freiburg), allowing compatibility with commercial QA phantoms. Alanine and film dosimeters were calibrated under conventional electron and proton beams, traceable to absorbed dose to water from Co-60 gamma rays. The unified dosimeter was used to measure dose from a 9 MeV electron FLASH beam (Varian Clinac iX) and a 230 MeV proton FLASH beam (IBA machine), with alanine and film irradiated simultaneously at the same location.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The alanine dosimeter measured the dose per pulse, instantaneous dose rate, and mean dose rate at a source-to-surface distance of 100 cm for the electron FLASH beam as 0.99 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$ pm $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 0.02 Gy/pulse, 2.48 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;annotation&gt;$ times $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 10&lt;sup&gt;5&lt;/sup&gt; Gy/s, and 357 Gy/s, respectively. The EBT-XD film showed good agreement (within a 2.0% relative difference) in the 10–30-Gy range, whereas the HD-V2 indicated a larger difference (up to 5.9%) compared to the alanine dosimeter. The mean dose rate for the proton FLASH beam, measured by the alanine dosimeter, was 115.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}