Medical physics最新文献

{"title":"Physical phantom validation of clustering-initiated factorization in dynamic PET","authors":"Valerie Kobzarenko, Suzanne L. Baker, Mustafa Janabi, Woon-Seng Choong, Grant T. Gullberg, Youngho Seo, Rostyslav Boutchko, Debasis Mitra","doi":"10.1002/mp.17902","DOIUrl":"10.1002/mp.17902","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Dynamic positron emission tomography (PET) enables the quantification of physiological parameters of radiotracers employed in the investigation of neuropsychiatric disorders. We previously introduced a factor analysis-based algorithm, Cluster-Initialized Factor Analysis (CIFA), designed to overcome the problem of specifying reference regions. CIFA is capable of automatically extracting distinct radiotracer binding distributions across many modalities based on the differences in tracer dynamics, and thus can distinguish regions of specific- and non-specific binding without requiring prior segmentation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Our goal is to quantitatively validate the ability of CIFA to resolve different dynamic biological processes by comparing the output of the algorithm to an independent benchmark. As an intermediate goal, we aim to create a physical phantom capable of modeling unique aspects of dynamic imaging and to use this phantom as the benchmark in evaluating CIFA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>CIFA was used to reconstruct <sup>18</sup>F-flortaucipir dynamic brain PET datasets acquired at Lawrence Berkeley National Lab. The resulting factor curves served as the foundation for creating dynamic input time-activity curve (TAC) combinations in a physical brain phantom specifically constructed for this purpose. The phantom represented three components: two overlapping tissue types and free radiotracer, constructed with a combination of small hydraulic elements. The physical components were scanned separately to generate a library of images, allowing us to reproduce scans of any duration with prescribed dynamics and realistic partial volume effects. The phantom was designed to produce noisy instances with compartment mixing of dynamic scans with desired activity TACs for free, non-specifically bound, and specifically bound radiotracers. Ten distinct dynamic simulations with varying levels of TAC similarity were estimated with CIFA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We directly evaluated CIFA's performance in analyzing each of the 10 dynamic datasets by computing the Pearson correlation coefficient between the estimated outputs and the ground truth tissue TACs and corresponding tissue distributions. For seven out of 10 modeled dynamics, which captured the full spectrum of realistically expected tissue TAC shapes, the curve correlation of the specific binding tissue was above 95%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This work formulated an innovativ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192576","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":"Multimodal medical image-to-image translation via variational autoencoder latent space mapping","authors":"Zhiwen Liang, Mengjie Cheng, Jinhui Ma, Ying Hu, Song Li, Xin Tian","doi":"10.1002/mp.17912","DOIUrl":"10.1002/mp.17912","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Medical image translation has become an essential tool in modern radiotherapy, providing complementary information for target delineation and dose calculation. However, current approaches are constrained by their modality-specific nature, requiring separate model training for each pair of imaging modalities. This limitation hinders the efficient deployment of comprehensive multimodal solutions in clinical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To develop a unified image translation method using variational autoencoder (VAE) latent space mapping, which enables flexible conversion between different medical imaging modalities to meet clinical demands.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a three-stage approach to construct a unified image translation model. Initially, a VAE is trained to learn a shared latent space for various medical images. A stacked bidirectional transformer is subsequently utilized to learn the mapping between different modalities within the latent space under the guidance of the image modality. Finally, the VAE decoder is fine-tuned to improve image quality. Our internal dataset collected paired imaging data from 87 head and neck cases, with each case containing cone beam computed tomography (CBCT), computed tomography (CT), MR T1c, and MR T2W images. The effectiveness of this strategy is quantitatively evaluated on our internal dataset and a public dataset by the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Additionally, the dosimetry characteristics of the synthetic CT images are evaluated, and subjective quality assessments of the synthetic MR images are conducted to determine their clinical value.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The VAE with the Kullback‒Leibler (KL)-16 image tokenizer demonstrates superior image reconstruction ability, achieving a Fréchet inception distance (FID) of 4.84, a PSNR of 32.80 dB, and an SSIM of 92.33%. In synthetic CT tasks, the model shows greater accuracy in intramodality translations than in cross-modality translations, as evidenced by an MAE of 21.60 ± 8.80 Hounsfield unit (HU) in the CBCT-to-CT task and 45.23 ± 13.21 HU/47.55 ± 13.88 in the MR T1c/T2w-to-CT tasks. For the cross-contrast MR translation tasks, the results are very close, with mean PSNR and SSIM values of 26.33 ± 1.36 dB and 85.21% ± 2.21%, respectively, for the T1c-to-T2w translation and 26.03 ± 1.67 dB and 85.73% ± 2.66%, respectively, for the T2w-to-T1c translation. Dosimetric results indicate that all the gamma pass rates for synthetic CTs are higher than 99% f","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176273","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 GPU-accelerated Monte Carlo dose engine for external beam radiotherapy","authors":"Zihao Liu,&nbsp;Yuxiang Wang,&nbsp;Yiqun Han,&nbsp;Panpan Hu,&nbsp;Cheng Zheng,&nbsp;Bing Yan,&nbsp;Yidong Yang","doi":"10.1002/mp.17899","DOIUrl":"10.1002/mp.17899","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Accurate dose computation is crucial in intensity-modulated radiation therapy. Owing to its high accuracy, Monte Carlo method is considered the gold standard for radiation dose computation. Its efficiency, however, demands continuous improvement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to develop a GPU-accelerated Monte Carlo radiation dose engine (GARDEN) for fast and accurate dose computation in external beam radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In GARDEN simulation, photon and electron transport were modeled using Woodcock tracking and Class II condensed history technique, respectively. To enhance GPU computational efficiency, warp convergence optimization and coalesced access methods were employed. A novel linear accelerator (Linac) head model was established by incorporating a virtual source and a digital collimator system. The physics was verified against GEANT4 in both homogeneous and heterogeneous phantoms. The Linac head model was commissioned using data measured in a water tank and validated by comparing simulation with film doses for two alternating open and closed MLC patterns. Finally, computational efficiency and accuracy were further evaluated in clinical IMRT and VMAT treatment plans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>GARDEN was more than 2500 times faster than GEANT4, with dose differences less than 1% in both homogeneous water and heterogeneous water-lung-bone phantoms. Compared to commission data, the average differences in percentage depth dose curves were less than 1%, and the penumbra differences in lateral dose profiles were less than 1 mm for various radiation field sizes. For two MLC patterns, the gamma pass rates between GARDEN simulations and films were 98.78% and 97.30% at 2%/2 mm criteria, respectively. Both IMRT and VMAT treatment plans achieved gamma pass rates exceeding 99.23% at 3%/3 mm criteria compared to GEANT4 results, with GARDEN completing the dose calculations within 3 s at ∼1% uncertainty on an i9-13900K CPU and NVIDIA 4080 GPU.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The accuracy and efficiency of GARDEN has been benchmarked against GEANT4 and validated in both phantoms and clinical treatment plans. With its capability for fast and accurate dose computation, GARDEN shows strong potential for applications in treatment planning and quality assurance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176257","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":"Experimental validation of Geant4 nuclear interaction models in dose calculations of therapeutic carbon ion beams","authors":"Yihan Jia,&nbsp;Martina Favaretto,&nbsp;Lisa Hartl,&nbsp;Markus Stock,&nbsp;Dietmar Georg,&nbsp;Loïc Grevillot,&nbsp;Andreas F. Resch","doi":"10.1002/mp.17906","DOIUrl":"10.1002/mp.17906","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 choice of nuclear interaction models in Monte Carlo simulations affects the dose calculation accuracy for light ion beam therapy.&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 aimed to evaluate the dose calculation accuracy and simulation time of three GATE-RTiON/&lt;span&gt;Geant4&lt;/span&gt; physics lists for therapeutic carbon ion beams, assessing their suitability for independent dose calculation in patient-specific quality assurance (PSQA).&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 normalized beam models for physics lists QGSP_BIC_HP_EMZ, QGSP_INCLXX_HP_EMZ, and Shielding_EMZ were validated against measurements regarding the accuracy of range, spot size and reference dose. Normalized transversal dose profiles (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$D/D_{max}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and field size factor (FSF) were compared with measurements. The accuracy of simulated target dose in 103 fields (various energies, field sizes, depths, and dose gradient &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mo&gt;∇&lt;/mo&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$nabla _D$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; complexity) of energy-modulated scanned beams was evaluated at 3181 positions. The median of global dose difference &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$med(Delta _D)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; was calculated at different depth ranges.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183239","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":"Evaluation of the stability of radiomic features of non-irradiated organs utilizing fan-beam kilovoltage computed tomography","authors":"Andrew Lian,&nbsp;Trevor Ketcherside,&nbsp;An Liu,&nbsp;Chunhui Han,&nbsp;Karine A Al Feghali,&nbsp;Arjun Maniyedath,&nbsp;Arya Amini,&nbsp;Colton Ladbury","doi":"10.1002/mp.17914","DOIUrl":"10.1002/mp.17914","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;Although radiation oncologists obtain non-contrast computed tomography (CT) images in every treatment, their quality is often insufficient for radiomic analyses for monitoring response over the course of treatment. Newer linear accelerators, such as RefleXion X1, have higher-quality imaging, creating new opportunities for radiomic analysis throughout treatment.&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 best utilize the high-quality kilovoltage computed tomography (kVCT) scans of RefleXion X1 for radiomic analyses of cancerous tissue, radiomic features that remain consistent through treatment in normal organs must first be identified. Stable features can be used for normalization to calculate radiomic features in tumors, enabling monitoring of response during treatment and early adaptation if required.&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 kVCT localization images acquired over the course of treatment on RefleXion X1 were analyzed for a total of five patients. A total of five patients were scanned using the RefleXion X1 throughout treatment. The imaging used standardized acquisition parameters for all treatments to minimize variation. Images were acquired using “Body/Medium Dose/Slow Couch” parameters. The regions of interest (ROIs) for each organ were automatically segmented using an auto-segmentation system Medical Mind Inc. Daily CT images and structure files were imported into an Image Biomarker Standardization Initiative (IBSI) compliant radiomic software package (LifeX) to extract radiomic features. Four non-irradiated organs were used to analyze the repeatability of normal tissues: liver, spleen, heart, and spinal cord.  The intraclass correlation coefficient (ICC) using a 2-way mixed-effects model was used to measure repeatability, while the concordance correlation coefficient (CCC) was used to measure reproducibility.&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;Cutoff values were applied to the average ICC across patients and the average CCC across both patients and fractions. Forty features were identified with a cutoff value of 0.8, accounting for 82% of the original features. Using a cutoff value of 0.9, the subset of stable features was further reduced to 29, representing 59% of the original features.&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;A subset of several radiomic features extracted remained stable throughout treatment. Thus, radiomic analyses of cancerous tissue using RefleXion X1 imaging throughout treatment would be feasible as an ongoing assessment of response ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176268","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 vessel bifurcation landmark pair dataset for abdominal CT deformable image registration (DIR) validation","authors":"Edward R. Criscuolo,&nbsp;Zhendong Zhang,&nbsp;Yao Hao,&nbsp;Deshan Yang","doi":"10.1002/mp.17907","DOIUrl":"10.1002/mp.17907","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. DIRs of intra-patient abdominal CTs are among the most challenging registration scenarios due to significant organ deformations and inconsistent image content. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Acquisition and Validation Methods</h3>\u0000 \u0000 <p>Abdominal CT image pairs of 30 patients were acquired from several publicly available repositories as well as the authors’ institution with IRB approval. The two CTs of each pair were originally acquired for the same patient but on different days. An image processing workflow was developed and applied to each CT image pair: (1) Abdominal organs were segmented with a deep learning model, and image intensity within organ masks was overwritten. (2) Matching image patches were manually identified between two CTs of each image pair. (3) Vessel bifurcation landmarks were labeled on one image of each image patch pair. (4) Image patches were deformably registered, and landmarks were projected onto the second image. (5) Landmark pair locations were refined manually or with an automated process. This workflow resulted in 1895 total landmark pairs, or 63 per case on average. Estimates of the landmark pair accuracy using digital phantoms were 0.7 mm ± 1.2 mm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Format and Usage Notes</h3>\u0000 \u0000 <p>The data are published in Zenodo at https://doi.org/10.5281/zenodo.14362785. Instructions for use can be found at https://github.com/deshanyang/Abdominal-DIR-QA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential Applications</h3>\u0000 \u0000 <p>This dataset is a first-of-its-kind for abdominal DIR validation. The number, accuracy, and distribution of landmark pairs will allow for robust validation of DIR algorithms with precision beyond what is currently available.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176261","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":"AAPM task group report 351: Protocol for clinical reference dosimetry in external beam MR-guided radiotherapy","authors":"Arman Sarfehnia,&nbsp;Paola E. Alvarez,&nbsp;Maria R. Bellon,&nbsp;Hugo Bouchard,&nbsp;Leon de Prez,&nbsp;James E. Dolan,&nbsp;Geoffrey S. Ibbott,&nbsp;Victor Malkov,&nbsp;Bryan R. Muir,&nbsp;David W. O. Rogers,&nbsp;Bram van Asselen,&nbsp;Jochem W. H. Wolthaus,&nbsp;Poonam Yadav","doi":"10.1002/mp.17884","DOIUrl":"https://doi.org/10.1002/mp.17884","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Magnetic Resonance-guided Radiation Therapy (MRgRT) systems are increasingly used in clinical practice, necessitating a dedicated dosimetry protocol to address the challenges posed by the presence of strong magnetic fields.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>AAPM TG351 introduces a reference dosimetry protocol for external beam MRgRT systems, aligning with AAPM TG51 reports and incorporating elements from the IAEA TRS398 code of practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The protocol offers practical guidance on beam quality and dosimetry setup in the presence of strong magnetic fields. It reviews relevant literature to identify reference-class chambers suitable for external beam MRgRT systems and determines their beam quality conversion and other relevant correction factors through a detailed analysis of available data and associated uncertainties.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>AAPM TG351 offers a comprehensive dosimetry protocol for external beam MRgRT systems, addressing the unique challenges posed by the presence of magnetic fields. It includes detailed guidance for measurement setups, a list of reference-class chambers appropriate for reference dosimetry in MRgRT systems, and updated beam quality conversion and correction factors. Additionally, the protocol presents an in-depth analysis of the uncertainty budget, identifying key contributors and providing strategies to minimize uncertainties, thereby enhancing clinical accuracy and reliability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This protocol provides a practical, evidence-based reference for medical physicists conducting reference dosimetry in external beam MRgRT systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615381","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 novel network architecture for post-applicator placement CT auto-contouring in cervical cancer HDR brachytherapy","authors":"Yang Lei,&nbsp;Ming Chao,&nbsp;Kaida Yang,&nbsp;Vishal Gupta,&nbsp;Emi J. Yoshida,&nbsp;Tingyu Wang,&nbsp;Xiaofeng Yang,&nbsp;Tian Liu","doi":"10.1002/mp.17908","DOIUrl":"10.1002/mp.17908","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;High-dose-rate brachytherapy (HDR-BT) is an integral part of treatment for locally advanced cervical cancer, requiring accurate segmentation of the high-risk clinical target volume (HR-CTV) and organs at risk (OARs) on post-applicator CT (pCT) for precise and safe dose delivery. Manual contouring, however, is time-consuming and highly variable, with challenges heightened in cervical HDR-BT due to complex anatomy and low tissue contrast. An effective auto-contouring solution could significantly enhance efficiency, consistency, and accuracy in cervical HDR-BT planning.&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 develop a machine learning-based approach that improves the accuracy and efficiency of HR-CTV and OAR segmentation on pCT images for cervical HDR-BT.&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 proposed method employs two sequential deep learning models to segment target and OARs from planning CT data. The intuitive model, a U-Net, initially segments simpler structures such as the bladder and HR-CTV, utilizing shallow features and iodine contrast agents. Building on this, the sophisticated model targets complex structures like the sigmoid, rectum, and bowel, addressing challenges from low contrast, anatomical proximity, and imaging artifacts. This model incorporates spatial information from the intuitive model and uses total variation regularization to improve segmentation smoothness by applying a penalty to changes in gradient. This dual-model approach improves accuracy and consistency in segmenting high-risk clinical target volumes and organs at risk in cervical HDR-BT. To validate the proposed method, 32 cervical cancer patients treated with tandem and ovoid (T&amp;O) HDR brachytherapy (3–5 fractions, 115 CT images) were retrospectively selected. The method's performance was assessed using four-fold cross-validation, comparing segmentation results to manual contours across five metrics: Dice similarity coefficient (DSC), 95% Hausdorff distance (HD&lt;sub&gt;95&lt;/sub&gt;), mean surface distance (MSD), center-of-mass distance (CMD), and volume difference (VD). Dosimetric evaluations included D90 for HR-CTV and D2cc for OARs.&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 proposed method demonstrates high segmentation accuracy for HR-CTV, bladder, and rectum, achieving DSC values of 0.79 ± 0.06, 0.83 ± 0.10, and 0.76 ± 0.15, MSD values of 1.92 ± 0.77 mm, 2.24 ± 1.20 mm, and 4.18 ± 3.74 mm, and absolute VD values of 5.34 ± 4.85 cc, 17.16 ± 17.38 cc, and 18.54 ± 16.83 cc, respectively. Despite challenges in bowel and sigmoid segmentation due to poor soft tissue contrast in CT and var","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145309","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":"Scenario-free robust optimization algorithm for IMRT and IMPT treatment planning","authors":"Remo Cristoforetti,&nbsp;Jennifer Josephine Hardt,&nbsp;Niklas Wahl","doi":"10.1002/mp.17905","DOIUrl":"10.1002/mp.17905","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;Robust treatment planning algorithms for intensity modulated proton therapy (IMPT) and intensity modulated radiation therapy (IMRT) allow for uncertainty reduction in the delivered dose distributions through explicit inclusion of error scenarios. Due to the curse of dimensionality, application of such algorithms can easily become computationally prohibitive.&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 work proposes a scenario-free probabilistic robust optimization algorithm that overcomes both the runtime and memory limitations typical of traditional robustness algorithms.&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 scenario-free approach minimizes cost-functions evaluated on expected-dose distributions and total variance. Calculation of these quantities relies on precomputed expected-dose-influence and total-variance-influence matrices, such that no scenarios need to be stored for optimization. The algorithm is developed within matRad and tested in several optimization configurations for photon and proton irradiation plans. A traditional robust optimization algorithm and a margin-based approach are used as a reference to benchmark the performance of the scenario-free algorithm in terms of plan quality, robustness, and computational workload.&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 implemented scenario-free approach achieves plan quality similar to traditional robust optimization algorithms, and it reduces the distribution of standard deviation within selected structures when variance reduction objectives are defined. Avoiding the storage of individual scenario information allows for the solution of treatment plan optimization problems, including an arbitrary number of error scenarios. The observed computational time required for optimization is close to a nominal, non-robust algorithm and substantially lower compared to the traditional robust approach. Estimated gains in relative runtime range from approximately &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$hskip.001pt 5$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;–&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;600&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$hskip.001pt 600$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; times with respect to the traditional approach.&lt;/p&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145320","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":"Reference datasets for commissioning of model-based dose calculation algorithms for electronic brachytherapy","authors":"Iymad R. Mansour,&nbsp;Christian Valdes-Cortez,&nbsp;David Santiago Ayala Alvarez,&nbsp;Francisco Berumen,&nbsp;Jean-Simon Côte,&nbsp;Gaël Ndoutoume-Paquet,&nbsp;Peter G. F. Watson,&nbsp;Jan Seuntjens,&nbsp;Facundo Ballester,&nbsp;Ernesto Mainegra-Hing,&nbsp;Rowan M. Thomson,&nbsp;Luc Beaulieu,&nbsp;Javier Vijande","doi":"10.1002/mp.17872","DOIUrl":"10.1002/mp.17872","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work provides the first two clinical test cases for commissioning electronic brachytherapy (eBT) model-based dose calculation algorithms (MBDCAs) for skin irradiation using surface applicators.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Acquisition and Validation Methods</h3>\u0000 \u0000 <p>The test cases utilize the INTRABEAM 30 mm surface applicator. <i>Test Case I</i>: <i>water phantom</i> is used to evaluate the algorithm's performance in a uniform medium consisting of a voxelized water cube surrounded by air. <i>Test Case II: Surface eBT</i> represents a heterogeneous medium with four distinct layers: skin tissue, adipose tissue, cortical bone, and soft tissue. Treatment plans for both cases were created and exported into the Radiance treatment planning system (TPS). Dose-to-medium calculations were then performed using this Monte Carlo (MC)-based TPS and compared with MC simulations conducted independently by three different groups using two codes: EGSnrc and PENELOPE. The results agreed within expected Type A and B statistical uncertainties.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Format and Usage Notes</h3>\u0000 \u0000 <p>The dataset is available online at https://doi.org/10.52519/00005. A proprietary file designed for use within Radiance containing CT images and the treatment plan for both test cases, the LINAC modeling, and the CT calibration are included, as well as reference MC and TPS dose data in RTdose format and all files required to run the MC simulations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential Applications</h3>\u0000 \u0000 <p>This dataset serves as a valuable resource for commissioning eBT MBDCAs and lays the groundwork for developing clinical test cases for other eBT systems. It is also a helpful educational tool for exploring various eBT devices and their advantages and drawbacks. Furthermore, brachytherapy researchers seeking a benchmark for dosimetric calculations in the low-energy domain will find this dataset indispensable.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145315","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}