Medical physics最新文献

{"title":"Design simulation of high-homogeneity portable MRI magnet array using global optimization algorithm and equivalent currents model","authors":"Jiannan Zhou, Xia Xiao, Yiming Liu, Chang Sun, Yu Liu, Xinyu Ma, Jiahui Ding, Yanwei Pang, Zhenchang Wang","doi":"10.1002/mp.17856","DOIUrl":"10.1002/mp.17856","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>High-field magnetic resonance imaging (MRI) systems offer high sensitivity and resolution but are costly and bulky, limiting their widespread use, particularly in remote areas. Conversely, portable MRI systems have emerged as a complementary technology, promising enhanced accessibility.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study introduces a novel optimization method combining an analytical model with a highly convergent global optimization algorithm to enhance the design of portable MRI permanent magnet arrays. The approach aims to significantly improve the efficiency of the magnet design process, thereby advancing the homogeneity of portable MRI magnet array.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed approach begins with the calculation of initial magnetic field distributions using current element principles. This is followed by the development of an advanced analytical model based on matrix algebra. The consistency between the calculated results of the analytical model and the results from finite element method (FEM) simulations is then evaluated to assess the reliability of the magnetic field calculations across various magnet array configurations. The integration of the analytical model with the improved grey wolf optimization (IGWO) algorithm enhances the optimization process, leading to magnet array configurations with improved homogeneity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>FEM simulations agree with the analytical model, revealing a computational error with an average root mean square error (RMSE) of 0.4% in the magnetic field map. The calculation speed of analytical model is at least 200 times higher than that using FEM-based software with uncompromised accuracy. The optimization process successfully yields a permanent magnet array with exceptional homogeneity (1080 ppm) and strong field strength (79.5 mT) across a 0.2 m diameter of spherical volume (DSV). Moreover, this is accomplished while maintaining a lightweight (129 kg) and compact design (interior diameter: 0.31 m). The IGWO model has been shown to outperform the benchmark genetic algorithm (GA) model, which is currently used for magnet design in MRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study introduces a novel optimization method that significantly enhances the design of portable MRI permanent magnet arrays. By integrating an analytical model with the IGWO algorithm, this method enhances the efficiency of magnet design compared to traditional FEM. This method addresses the limitation","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3760-3771"},"PeriodicalIF":3.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048700","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 technique for achieving arbitrary machine dose rates via MLC leaf motion modulation using fixed-gantry IMRT delivery mode","authors":"Xin Wu, Dongrong Yang, Qingrong Jackie Wu, Yang Sheng, Anna E. Rodrigues, Joseph P. Kowalski, Qiuwen Wu","doi":"10.1002/mp.17835","DOIUrl":"10.1002/mp.17835","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The medical linear accelerator (Linac) typically operates at a constant but discrete dose rate for both 3D conformal fields and intensity modulation radiotherapy (IMRT) fields. However, in certain clinical and radiobiological scenarios, such as total body irradiation (TBI), an arbitrary dose rate outside the preset values may be desired.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to achieve arbitrary machine dose rates in photon delivery through modulation of MLC leaf motion, providing flexibility for specific needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The desired effective dose rate is achieved by precisely programming the motion of unused MLC leaves outside the field boundary set by the jaws, while keeping the MLC leaves within the jaw boundary same as originally planned. Between control points, the leaf speed is adjusted by multiplying the permissible limit by the ratio of the current dose rate to the desired value. During delivery, the dose rate is reduced so that the MLC leaves arrive at the expected position for the expected monitor units (MU). Two schemes were designed for different application scenarios and successfully delivered and verified. A circular 3D conformal field, a 40 cm × 40 cm TBI field, and a head-and-neck (H&N) IMRT DMLC field were modified for dose rates between 600 and 1 MU/min. These plans were successfully delivered on Varian TrueBeam, and Clinac Linacs with modulated dose rates. Trajectory log analysis and portal dosimetry QA (PDQA) were utilized to verify the accuracy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Both schemes achieved stable and accurate dose rates, with mean deviations from the desired values remaining below 0.5% across the range from 600 to 1 MU/min. Fluctuations, represented by relative standard deviations, increased monotonically as the desired dose rate decreased. For clinically relevant dose rates above 100 MU/min, fluctuations remained below 0.5%. At lower dose rates, the thresholds for fluctuations reaching 1% and 10% were approximately 50 and 7 MU/min, respectively. Trajectory log file analysis confirmed agreement between planned and actual values of various treatment parameters, such as leaf position, speed, and MU. PDQA analysis showed 99% gamma pass rate with criteria of 3%/2 mm. For Scheme 1 with step-and-shoot, a 50% increase in delivery time due to the temporary beam-hold during step-and-shoot was both expected and observed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A technique has been developed and validated to","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3595-3610"},"PeriodicalIF":3.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002141","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":"Medical physics dataset article: A database of FLASH murine in vivo studies","authors":"Mathilde Toschini,&nbsp;Isabella Colizzi,&nbsp;Antony John Lomax,&nbsp;Serena Psoroulas","doi":"10.1002/mp.17744","DOIUrl":"10.1002/mp.17744","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The FLASH effect refers to a lower normal tissue damage for an equivalent tumor response, potentially widening the therapeutic window for radiotherapy. Although this effect has been demonstrated in various experiments using different types of particles and irradiation parameters, the underlying mechanism is not yet clearly understood. Uncertainties surround the conducted experiments, the explored parameter space, and the variability of reported results. To gain a better overview, we have created a dataset that includes in vivo FLASH experiments. This dataset documents all machine and biological dosimetric parameters, and for determined endpoints, it includes the outcome of the experiment. Our goal with this database is to increase awareness of the results and their variability and provide a useful research and analysis tool for the community.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Acquisition and Validation Methods</h3>\u0000 \u0000 <p>The database contains peer-reviewed papers published until March 2024 on the FLASH in vivo (murine) experiments. From each paper, previously defined parameters have been manually extracted and/or recalculated to ensure compatibility within the database entries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Format and Usage Notes</h3>\u0000 \u0000 <p>We provide two types of datasets: a user-friendly web-based Notion database and spreadsheets on a Zenodo repository. The database contains all the reviewed papers with extracted information in text or numeric form. Users can duplicate the database or view, search, filter, and reorganize online entries. The spreadsheets contain the data for the most analyzed endpoints (skin toxicity, survival rate, and crypt cells), allowing a comparative analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential Applications</h3>\u0000 \u0000 <p>The study has two main applications. The web-based database will allow for a user-friendly search of information and metadata of all published FLASH murine data. This will facilitate future research efforts to better understand the FLASH effect. The spreadsheets are a simple and useful tool for the community to conduct statistical analysis and determine the parameters associated with the FLASH effect.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"5115-5123"},"PeriodicalIF":3.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17744","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040250","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":"Optimization of microwave hyperthermia system for focused breast cancer treatment: A study using realistic digital breast phantoms","authors":"Burak Acar,&nbsp;Tuba Yilmaz,&nbsp;Ali Yapar","doi":"10.1002/mp.17836","DOIUrl":"10.1002/mp.17836","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;Microwave breast hyperthermia is a noninvasive treatment method for breast cancer that utilizes microwave energy (ME) sources to raise tissue temperatures above 42&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$^{circ }{rm C}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, inducing tumor cell necrosis. The efficiency of ME deposition depends on the electric field magnitude and tissue conductivity, with antenna phase and amplitude adjustments used to maximize the electric field magnitude within tumors. Achieving precise ME focusing in the complex and heterogeneous breast tissue is challenging and can lead to unwanted hot spots in normal tissue. This study presents a novel method for optimizing ME focusing on the center of target tumors, using a simplified calculation of antenna phases, heuristic optimization for antenna amplitudes, and realistic breast phantoms for performance evaluation.&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;In this work, we propose an approach to optimize the microwave hyperthermia system, employing phase and amplitude modulation techniques to concentrate the electric field at the center of a malignant tumor within a breast medium. The approach uses line sources arranged in a circular pattern around realistic breast models. The method begins by determining the phase, followed by adjusting the amplitudes of each source in order to maximize the total electric field at the tumor's center. The goal is to maximize the electric field at the tumor center while minimizing the optimization cost and complexity.&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;Simulations are performed at 4 GHz frequency using two different types of digital breast phantoms (fatty and dense breasts) as test beds. The algorithm is tested by using three quantities; that is, the electric field distribution, the power density distribution, and the temperature distribution inside the whole breast region. The electric field and power density are calculated using an in-house method of moments (MoM) algorithm, while the temperature distributions are obtained with computer simulation technology (CST) software. To further evaluate the method with quantitative measures of success, thermal indices are calculated for each phantom and method.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3557-3569"},"PeriodicalIF":3.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001048","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":"A clustering-based approach to address correlated features in predicting genitourinary toxicity from MRI-guided prostate SBRT","authors":"Pouyan Rezapoor,&nbsp;Jonathan Pham,&nbsp;Beth Neilsen,&nbsp;Hengjie Liu,&nbsp;Minsong Cao,&nbsp;Yingli Yang,&nbsp;Ke Sheng,&nbsp;Ting Martin Ma,&nbsp;James Lamb,&nbsp;Michael Steinberg,&nbsp;Amar U. Kishan,&nbsp;Zachary Taylor,&nbsp;Dan Ruan","doi":"10.1002/mp.17834","DOIUrl":"10.1002/mp.17834","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;It is common in outcome analysis to work with a large set of candidate prognostic features. However, such high-dimensional input and relatively small sample size leads to risk of overfitting, low generalizability, and correlation bias.&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 addresses the issue of correlation bias mitigation in the context of predicting genitourinary (GU) toxicity in prostate cancer patients underwent MRI-guided stereotactic body radiation therapy (SBRT).&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;Typical dimension reduction or feature selection methods include regularization for sparsity or information criterion. However, when heavy correlation occurs with (subsets of) input features, the assigned weights of correlated features can be diluted to an extent that the corresponding features are no more effective in the prediction, leading to suboptimal feature discovery and prediction. We propose to perform advanced hierarchical clustering and then apply regression modeling to cluster centroids. This approach addresses the challenges posed by high dimensionality and ill-conditioning, and improves accuracy and reliability of the resulting prediction models. Performance of the proposed method was evaluated on typical regression models with intrinsic feature reduction methods, namely Least Absolute Shrinkage and Selection Operator (LASSO) regularized logistic regression (LR), support vector machine (SVM), and decision trees (DT).&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;Extensive experiments show that introducing cluster-based feature compaction and representation improves all regression models under fair hyperparameter tuning conditions. Although LASSO and LR with clustered features had similar performance during training and validation, with LASSO-LR being slightly better, the cluster-based feature method achieved significantly better performance on the test set by achieving 0.91 AUC and 0.86 accuracy, demonstrating its advantage in stability and robustness. The overall best test performance is achieved by combining feature clustering to five representatives with SVM. Additional correlation study identified individual features closely representing the cluster centroids as exposure volume of rectum at 2 Gy rectum, trigone exposure at 2 Gy and 41 Gy, urethra at 42 Gy urethra, and rectal wall at 42 Gy rectal wall. This indicates the importance of hot spot control of urethra, trigone, and rectal wall for toxicity control.&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;These f","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"5104-5114"},"PeriodicalIF":3.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046722","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":"Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images","authors":"Mehan Haidari,&nbsp;Elsayed Ali,&nbsp;Dal Granville","doi":"10.1002/mp.17838","DOIUrl":"10.1002/mp.17838","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 extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.&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 was dedicated to creating a model capable of rapidly generating sCT images from CBCT scans, specifically for the entire spine. This work aims to be a step towards a CT simulation-free workflow by using onboard imaging for real-time palliative radiotherapy treatments for patients with spinal metastases.&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;Using CBCT and planning fan-beam CT images from 220 patients, we developed and validated a two-stage sCT generation model. The initial stage used a conditional generative adversarial network (GAN) to minimize streaking artifacts in CBCT images, using 7400 images for training and 1000 for validation. The second stage used a cycle-consistent GAN to produce sCT images, training on 14,700 images and validating on 500 images. The quality of the sCT images was evaluated quantitatively using a distinct dataset from 33 patients who received same-day palliative radiotherapy for spinal metastases.&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;Our two-stage model generated high-quality sCT images from CBCT scans across the entire spine, significantly improving HU accuracy and dosimetric agreement with planning CT images. Mean Absolute Error was reduced from 225 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$,pm,$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 62 HU in CBCT to 86 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$,pm,$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 24 HU in sCT images, and Mean Error was improved from 178 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$,pm,$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 91 HU to −8 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4134-4146"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061450","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":"Two-step beam geometry optimization for volumetric modulated arc therapy gantry angles in breast treatments","authors":"Mikko Hakala,&nbsp;Luca Cozzi,&nbsp;Elena Czeizler","doi":"10.1002/mp.17788","DOIUrl":"10.1002/mp.17788","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 partial arc volumetric modulated arc therapy (VMAT) for treating breast cancer, setting up the limiting gantry positions of the treatment machine is a nontrivial yet repetitive and time-consuming task during planning. Templatized solutions exist but may not provide adequate plan quality.&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;We have developed a two-step beam geometry optimization (2SBGO) method to set up in an automated manner the VMAT start/stop gantry angles and avoidance sector (AvS) angle limits for breast treatments. We compare our preliminary results of 2SBGO to manually created plans.&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 the first step of the method (based on patient geometry), the initial angles are obtained either from a template, from machine-learning (ML) predictions or manually. A search range around the initial positions is specified for each angle. In the second step (refinement using dosimetric criteria), the parameters are optimized using generalized simulated annealing (GSA). As objective function for GSA, we used the optimizer cost. We tested the method for deep inspiration breath hold and free breathing patients for left- and right-sided breast treatments. As ML models, we trained convolutional neural networks to predict the angles (start/stop angles for both the partial arc and the avoidance sector limits). The training set size was up to 86 patients, the validation set size was fixed to six patients and the test set size to six patients. The initial input before preprocessing was in DICOM format (RT plan and structure files and CT images). The rationale for using ML as first step is to learn from data the ways the beam angles are set and evaluate how good the initial ML solution would be for the final plan quality.&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;We showed that for all the test patients, the 2SBGO leads to plans that are of a comparable dosimetric quality compared with manual plans while eliminating the complex and time-consuming beam geometry (BG) set-up step. Additionally, with the optimization function we used in our approach the ipsilateral lung doses in right-sided treatments are reduced compared with plans with manual angle selection. The ML models were shown to be most useful in a clinical workflow when integrated in the full 2SBGO scheme. The ML models themselves, before the second optimization step, predicted the medial angle within accuracy 3.6° ± 2.6° (mean ± SD) and the AvS limiting values within 10.8° ± 8.3°. The Wilcoxon paired signed-rank test indicated that there were no statistically significant differences between th","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4984-4995"},"PeriodicalIF":3.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032874","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":"U-shaped network combining dual-stream fusion mamba and redesigned multilayer perceptron for myocardial pathology segmentation","authors":"Wenjie Zhang,&nbsp;Tiejun Yang,&nbsp;Jiacheng Fan,&nbsp;Heng Wang,&nbsp;Mingzhu Ji,&nbsp;Huiyao Zhang,&nbsp;Jianyu Miao","doi":"10.1002/mp.17812","DOIUrl":"10.1002/mp.17812","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;Cardiac magnetic resonance imaging (CMR) provides critical pathological information, such as scars and edema, which are vital for diagnosing myocardial infarction (MI). However, due to the limited pathological information in single-sequence CMR images and the small size of pathological regions, automatic segmentation of myocardial pathology remains a significant challenge.&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;In the paper, we propose a novel two-stage anatomical-pathological segmentation framework combining Kolmogorov–Arnold Networks (KAN) and Mamba, aiming to effectively segment myocardial pathology in multi-sequence CMR images.&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;First, in the coarse segmentation stage, we employed a multiline parallel MambaUnet as the anatomical structure segmentation network to obtain shape prior information. This approach effectively addresses the class imbalance issue and aids in subsequent pathological segmentation. In the fine segmentation stage, we introduced a novel U-shaped segmentation network, KANMambaNet, which features a Dual-Stream Fusion Mamba module. This module enhances the network's ability to capture long-range dependencies while improving its capability to distinguish different pathological features in small regions. Additionally, we developed a Kolmogorov–Arnold Network-based multilayer perceptron (KAN MLP) module that utilizes learnable activation functions instead of fixed nonlinear functions. This design enhances the network's flexibility in handling various pathological features, enabling more accurate differentiation of the pathological characteristics at the boundary between edema and scar regions. Our method achieves competitive segmentation performance compared to state-of-the-art models, particularly in terms of the Dice coefficient.&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;We validated our model's performance on the MyoPS2020 dataset, achieving a Dice score of 0.8041 &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; 0.0751 for myocardial edema and 0.9051 &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; 0.0240 for myocardial scar. Compared to the baseline model MambaUnet, our edema segmentation performance improved by 0.1420, and scar segmentation performance improved by 0.1081.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4567-4584"},"PeriodicalIF":3.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050676","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":"Fraction-variant beam orientation optimization for spatiotemporal fractionation schemes","authors":"Nathan Torelli,&nbsp;Jan Unkelbach","doi":"10.1002/mp.17798","DOIUrl":"10.1002/mp.17798","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;Spatiotemporal fractionation schemes aim at partial hypofractionation in the tumor along with more uniform fractionation in the normal tissue. This is achieved by delivering distinct dose distributions in different fractions, where each fraction contributes with high doses to complementary parts of target volume, while similar dose baths are delivered to the normal tissue.&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;In this study, we extend spatiotemporal fractionation schemes by allowing for different non-coplanar beam orientations in each fraction. As different regions of the target volume are treated in different fractions, spatiotemporal fractionation schemes may benefit from selecting different beam orientations in each fraction.&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;Spatiotemporally fractionated (STF) treatments are generated using a novel biologically effective dose (BED)-based direct aperture optimization (DAO) algorithm, which allows for the simultaneous optimization of multiple-dose distributions to be delivered in the different fractions along with their corresponding sets of multileaf collimated apertures and monitor unit (MU) weights. Each set of apertures specifies a series of control points along a fraction-specific non-coplanar dynamic trajectory, which consists of a 360° gantry arc with dynamic bi-directional couch rotation. The gantry-couch path is automatically determined during the treatment plan optimization. The proposed DAO approach is demonstrated for stereotactic body radiotherapy (SBRT) of a patient with liver metastases (3 × 12 Gy, patient 1) and stereotactic radiotherapy (SRT) of a patient with a large arteriovenous malformation (AVM) (4 × 7 Gy, patient 2).&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;For both patients, STF treatments deliver highly non-uniform dose distributions in distinct fractions. Depending on which region of the target volume is irradiated in each fraction, very different non-coplanar dynamic trajectories are selected. For a similar target coverage, the STF treatment obtained using fraction-specific non-coplanar dynamic trajectories reduces the mean liver BED&lt;sub&gt;3&lt;/sub&gt; in patient 1 by 13.7% (32.9 Gy vs. 38.1 Gy) and the mean brain BED&lt;sub&gt;2&lt;/sub&gt; in patient 2 by 18.4% (5.15 Gy vs. 6.31 Gy), respectively, compared to a STF treatment obtained using coplanar VMAT arcs in every fraction.&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;The dosimetri","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4723-4741"},"PeriodicalIF":3.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004121","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":"Patient-specific modeling of radiation-induced lymphopenia for head and neck cancer","authors":"Jiaxin Li,&nbsp;Rahul Mali,&nbsp;Gregory N. Gan,&nbsp;Christopher Lominska,&nbsp;Kenny Guida,&nbsp;Aditya Juloori,&nbsp;Matthew Wen-Ruey Chen,&nbsp;Wangyao Li,&nbsp;Jufri Setianegara,&nbsp;Chao Wang,&nbsp;Yuting Lin,&nbsp;Qiang Li,&nbsp;Weiqiang Chen,&nbsp;Hao Gao","doi":"10.1002/mp.17829","DOIUrl":"10.1002/mp.17829","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Radiation-induced lymphopenia (RIL) is a frequent complication in head and neck cancer (HNC) patients undergoing radiotherapy (RT), and its severity is associated with poorer survival outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work aims to develop a patient-specific modeling method to simulate lymphocyte kinetics during and after RT and evaluate the lymphocyte-sparing effects across different RT treatment regimens.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cohort of 17 HNC patients receiving unilateral irradiation with protons or photons were included in this study. The dose to circulating lymphocytes was calculated using the HEDOS model, considering lymph nodes on the irradiated side, the esophagus, auto-segmented bilateral carotid arteries and jugular veins, skeletal muscle, fat, skin, compact bone, spongy bone, red marrow, and other skeleton. A patient-specific model was developed to simulate lymphocyte kinetics that account for radiation-induced damage to both circulating lymphocytes and lymph nodes. The weekly absolute lymphocyte counts (ALC) before, during and after RT, were assembled to estimate the patient-specific parameters. Four different RT treatment regimens—conventional fractionation, hypofractionation, stereotactic body radiotherapy (SBRT), and FLASH—were evaluated to compare their lymphocyte-sparing effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Patients treated with protons had 17.1% less grade 3 and 4 RIL compared to photons. The mean dose to circulating lymphocytes was 1.28 ± 0.37 Gy(RBE) for proton therapy and 3.12 ± 0.75 Gy for photon therapy. The patient-specific model captured three distinct patterns of ALC kinetics: plateau phase, normal recovery, and incomplete recovery, with a mean squared error (MSE) of 0.024 ± 0.025 (mean ± SD) between the simulated and observed ALC values. On average, 42.72% of circulating lymphocytes received more than 0.1 Gy(RBE) in proton FLASH, significantly less than the 81.94% in photon FLASH. Hypofractionated RT, SBRT, and FLASH were 6.5%, 20.2%, and 29.9%, respectively, higher than conventional RT in term of ALC levels 3 months post-RT. At 1 year post-RT, most patients achieved at least 70% recovery of baseline ALC for all treatment regimens.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A patient-specific method has been developed for modeling lymphocyte dynamics over the course of RT and the subsequent follow-up period for HNC patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3583-3594"},"PeriodicalIF":3.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052528","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}