Medical physics最新文献

{"title":"FLASH effect on the liver and spleen of mice—a transcriptional perspective","authors":"Yuqi Ma, Xinlin Zhu, Tianyu Yang, Jianfeng Lv, Xueqing Yan, Gen Yang, Xiaoji Lin, Zhifei Cao","doi":"10.1002/mp.70720","DOIUrl":"https://doi.org/10.1002/mp.70720","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>FLASH radiotherapy (FLASH-RT), delivered at an ultra-high dose rate (typically ≥ 40 Gy/s), has been shown to reduce normal tissue toxicity compared with conventional dose-rate radiotherapy (CONV-RT). However, the transcriptomic mechanisms underlying FLASH-mediated protection remain incompletely understood.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to compare the biological effects of FLASH and CONV irradiation on mouse liver and spleen tissues and to identify molecular pathways associated with the FLASH effect from a transcriptional perspective.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>C57BL/6J male mice received a single 12 Gy abdominal dose delivered at either 2 Gy/min (CONV) or 100 Gy/s (FLASH). Twelve days after irradiation, spleen and liver tissues were harvested for histological section analysis and RNA-seq. Inflammatory responses in the spleen and liver were assessed by hematoxylin-eosin staining (H&E staining) and immunohistochemical quantification. A series of bioinformatic analyses, such as gene enrichment, protein–protein interaction (PPI) analysis, and transcription factor prediction, were performed to explore the effect of FLASH irradiation. Subsequently, reverse transcription quantitative PCR (RT-qPCR), western blot analysis, and immunohistochemical quantification were used to verify the bioinformatic results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Histological and immunohistochemical analyses showed less prominent inflammatory changes and lower immune-cell marker staining in mouse spleen and liver after FLASH than after CONV irradiation at 12 days post-irradiation. Bioinformatic analysis and corresponding experimental results revealed that compared to CONV irradiation, FLASH irradiation induced down-regulation of oxidative phosphorylation and cytoplasmic translation pathways in both mouse liver and spleen. Meanwhile, immune-related pathways, especially interferon β (IFNβ) signaling pathways, were significantly activated in both tissues after FLASH than after CONV irradiation. For these three pathways, key transcription factors were further identified and verified. Notably, STAT1 was found to play a potentially critical role in the upregulation of the IFNβ-related pathway under FLASH irradiation conditions in both liver and spleen.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>FLASH irradiation induces distinct metabolic and immune adaptations in normal mouse liver and spleen tissues compared with CONV irradiation. The coordinated suppression of metabolic an","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149222832","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":"Artificial intelligence apps for medical image analysis using pyCERR and cancer genomics cloud","authors":"Aditya P. Apte,&nbsp;Eve LoCastro,&nbsp;Aditi Iyer,&nbsp;Sharif Elguindi,&nbsp;Jue Jiang,&nbsp;Maria Thor,&nbsp;Jung Hun Oh,&nbsp;Harini Veeraraghavan,&nbsp;Amita Shukla-Dave,&nbsp;Joseph O. Deasy","doi":"10.1002/mp.70701","DOIUrl":"https://doi.org/10.1002/mp.70701","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To provide a software resource that enables medical researchers and practitioners to develop and apply reproducible, out-of-the-box, AI-based analysis workflows for radiological and radiotherapy data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Development and validation methods</h3>\u0000 \u0000 <p>The framework comprises two main components: the Python-native computational environment for radiological research (pyCERR) for radiological image processing, and the cancer genomics cloud (CGC), a National Cancer Institute–funded resource that supports cloud-hosted collaborative projects, with tools for managing collaborators and compute-hardware from providers such as Amazon Web Services and Google Cloud Platform. pyCERR is open-source (GNU GPL) and distributed at https://github.com/cerr/pycerr; it provides user-friendly data structures, simplified access to metadata across radiological imaging modalities, and tools for data transformation, AI-based image segmentation, radiomics, dynamic contrast-enhanced MRI analysis, radiotherapy dose–volume histogram (DVH) feature extraction, and normal-tissue complication and tumor-control models. Radiomics features comply with the Image biomarker standardization initiative (IBSI) standards and are validated against published reference values. Unit tests covering data input/output, radiomics, DVH features, and visualization run on every code update. Workflows are containerized for reproducible, identical execution on the CGC and on local hardware. The CGC enables AI inference from radiological images to be combined with other data types such as genomics and proteomics. Our public CGC project (https://cgc.sbgenomics.com/u/sevenbridges/pycerr-radiological-radiotherapy-imageanalysis), accessible with free registration, currently includes apps for organ-at-risk (OAR) and lung-nodule segmentation based on published models trained in-house and clinically validated. These apps can be run interactively from a web browser or programmatically via a REST API with client support for multiple programming languages. Notebooks demonstrating the CGC API and pyCERR capabilities are provided at https://github.com/cerr/pycerr-notebooks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential applications</h3>\u0000 \u0000 <p>The framework facilitates end-to-end radiological image analysis and reproducible research, including data access from public and private repositories, training and inference with AI models, and utilities for data management, visualization, and simplified access to image metadata.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149222833","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":"End-to-end automated radiotherapy planning using transfer learning to overcome data scarcity","authors":"Lu Jiang,&nbsp;Emily Hirata,&nbsp;Evan Porter,&nbsp;Di Xu,&nbsp;Jiayi Du,&nbsp;Wensha Yang,&nbsp;Qihui Lyu,&nbsp;Minsong Cao,&nbsp;Ke Sheng","doi":"10.1002/mp.70704","DOIUrl":"10.1002/mp.70704","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To develop and benchmark 3D dose prediction algorithms for volumetric modulated arc therapy for head-and-neck (HN) and pancreatic cancer treatments, and to demonstrate the clinical feasibility of converting the predicted doses into deliverable plans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and materials</h3>\u0000 \u0000 <p>Three deep learning models were pretrained on 3232 public plans and fine-tuned on institutional cohorts (256 HN and 72 pancreas patients). The accuracy of the fine-tuning strategy was compared with the baseline trained from scratch using only institutional data. In a prospective setting, the predicted dose was used as the planning goal for fully automated plan generation. Automated plans were benchmarked against clinical plans using Plan Quality Metrics. The deliverability of the fully automated plans was verified by Mobius ion-chamber patient-specific quality assurance (PSQA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Relative to training from scratch, transfer learning significantly accelerated convergence and minimized prediction error in scenarios with limited site-specific data. For HN cases, fine-tuning yielded a prediction error of 1.76 Gy. Notably, models fine-tuned on just 5% (<i>n</i> = 12) of the dataset achieved accuracy comparable to models trained from scratch using 50% (<i>n</i> = 116) of the training data. In the pancreas cohort, fine-tuning resulted in a mean absolute error of 1.35 Gy, representing a 14.6% reduction compared to the baseline trained solely on institutional data. When used for automated planning, these predicted 3D doses improved planning target volume homogeneity and organs at risk sparing (esophagus and trachea) in HN plans, while pancreas plans aimed to meet clinical goals with reduced dose to the spinal cord and right kidney. All automated plans were confirmed as clinically deliverable via PSQA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Compared with models trained from scratch on institutional data, transfer learning significantly reduced prediction error and improved robustness. The feasibility of converting predicted doses into clinical plans while maintaining plan quality through a fully automated process was demonstrated.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198165","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":"Quantitative and qualitative magnetic resonance imaging-based analysis to predict postoperative disease progression in hepatocellular carcinoma","authors":"He Wang,&nbsp;Minggang Yin,&nbsp;Zhiyan Chen,&nbsp;Feixiang Zou,&nbsp;Yuan Chen,&nbsp;Taixue An,&nbsp;Qiyang Li,&nbsp;Mingyue Cai,&nbsp;Siri Fu,&nbsp;Xiangrong Yu","doi":"10.1002/mp.70692","DOIUrl":"10.1002/mp.70692","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;Hepatocellular carcinoma (HCC) is one of the most common abdominal malignancies and the third leading cause of cancer-related death globally. Predicting the postoperative prognosis of HCC remains an important area of clinical research. Many qualitative magnetic resonance imaging (MRI) indicators have been used for noninvasive and global assessment of HCC for diagnostic and prognostic purposes. However, the prognostic value of quantitative MRI parameters has not been extensively validated in multicenter cohorts.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aimed to verify whether MRI-based imaging features could improve prognostic prediction in patients with HCC. We developed prognostic models to predict the progression-free survival (PFS) of patients with HCC.&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 multicenter cohort study, 203 patients diagnosed with early- to intermediate-stage HCC were divided into a training dataset and an external validation dataset. In addition to clinical indicators, the candidates included qualitative and quantitative MRI data. Three noninvasive models were developed: the Clinical Model, the Radiological Model, and the Combined Model. The patients were stratified according to the risk of disease progression. Model performance was assessed through discrimination and calibration analyses, decision curve analysis was performed, and a clinical application was designed. In addition, the subgroups at different stages of the disease were analyzed, and PFS was compared between the high- and low-risk groups.&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;Prognostic nutritional index, tumor-feeding vessels, mosaic architecture and the signal enhancement ratio (SER) parameter were identified as independent predictors of disease progression in patients with HCC. The Combined Model demonstrated superior performance compared with the Clinical and Radiological Models. The areas under the curve (AUCs) in the training dataset were 0.822, 0.748, and 0.795, whereas those in the validation dataset were 0.819, 0.758, and 0.677, respectively. The Combined Model also demonstrated favorable calibration and greater net clinical benefit on decision curve analysis. Its predictive ability remained consistent regardless of the Child–Pugh classification or Barcelona Clinic Liver Cancer stage.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study highlights the important role of quantitative and qualitative MRI assessments in evaluating the prognosis of patients with HCC and emphasize","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187141","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":"Low-spatial-frequency perception enhancing dynamic convolutional network for system matrix recovery in magnetic particle imaging","authors":"Zhen Dong,&nbsp;Yu An,&nbsp;Guanghui Li,&nbsp;Lishuang Guo,&nbsp;Jie Tian,&nbsp;Jiangang Liu","doi":"10.1002/mp.70702","DOIUrl":"10.1002/mp.70702","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;Magnetic particle imaging (MPI) is an emerging molecular imaging modality known for its high sensitivity and long-term tracking. System matrix (SM)-based image reconstruction is one of the most important reconstruction methods for MPI. However, MPI reconstruction based on the measured SM is limited by the requirement of voxel-stepwise precise calibration of system function within the field of view (FOV), which is time-consuming and noise-prone. Additionally, any changes in system properties require recalibrating the SM. This hinders the clinical translation and practical application of MPI.&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 these limitations, we propose a low-spatial-frequency perception enhancing dynamic convolutional network (LPEDCN) to recover SMs for accelerating calibration.&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;Specifically, we observe and reveal a positive correlation between the percentage of low-spatial-frequency energy and the signal-to-noise ratio (SNR) in the spatial Fourier transform (SFT) domain of real-world SM rows. Based on this prior, we design a low-spatial-frequency perception enhancement (LPE) joint loss to suppress high-frequency noise during model training. We also introduce a context-modulated dynamic convolution (CMDC) to capture long-range contextual features. We evaluate LPEDCN on simulated (training set: 60 SMs split into 107880 SM-row slices; validation set: 20 SMs split into 37520 slices; test set: 20 SMs split into 32660 slices; with no overlap between sets), public real-world OpenMPI (training set: 1 SM split into 101861 SM-row slices; validation set: 1 SM split into 62863 slices; test set: 1 SM split into 85347 slices; with no overlap between sets), and in-house (test set: 2 SMs split into 225 and 272 slices, respectively) datasets. SM recovery accuracy is evaluated by normalized root-mean-square error (nRMSE), and the reconstruction quality is evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). We assess LPEDCN by comparing it with seven state-of-the-art (SOTA) methods. Statistical significance is evaluated using two-tailed t-tests with &lt;i&gt;p&lt;/i&gt; &lt; 0.05 after False Discovery Rate (FDR) correction, and practical significance is assessed using Cohen's &lt;i&gt;d&lt;/i&gt; for effect size.&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;LPEDCN not only accelerates SM calibration in MPI, but also exhibits higher accuracy in SM recovery and better quality in image reconstruction compared to SOTA methods. For the core SM recovery metric nRMSE, LPEDCN significantly outperforms SOTA methods (&lt;i&gt;p&lt;/i&gt; &lt;","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187138","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":"Prediction of radiation-induced hypothyroidism after proton-based supraclavicular nodal irradiation in breast cancer","authors":"Jiayao Sun,&nbsp;Ping Li,&nbsp;Yongkuan Hao,&nbsp;Yinxiangzi Sheng,&nbsp;Weiwei Wang,&nbsp;Qing Zhang,&nbsp;Xiaomao Guo","doi":"10.1002/mp.70703","DOIUrl":"10.1002/mp.70703","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 thyroid gland lies adjacent to the supraclavicular target and is highly radiosensitive, placing patients at risk for radiation-induced hypothyroidism (RIHT). While RIHT incidence and predictive models have been well described after photon-based irradiation, evidence in the proton therapy setting remains limited.&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 evaluate the incidence of RIHT in breast cancer patients undergoing proton-based supraclavicular nodal irradiation and to develop proton-specific normal tissue complication probability (NTCP) models incorporating dose-volume, dose-averaged linear energy transfer (LETd)-volume, and dose–LETd–volume histogram (DLVH) metrics.&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;A total of 113 breast cancer patients treated with proton supraclavicular irradiation were retrospectively analyzed. Dose-volume, LETd-volume, and DLVH metrics were extracted for the thyroid gland and individual thyroid lobes. After exclusion of non-informative variables, univariable logistic regression models were developed for all retained candidate predictors. Internal validation and model selection were performed using 1000 bootstrap resamples, and the final bootstrap-selected predictor was defined as the variable with the highest selection frequency. Model performance was assessed using apparent and optimism-corrected area under the receiver operating characteristic curve (AUC). Biological dose metrics were additionally generated using six published LET-dependent relative biological effectiveness (RBE) models and compared with the fixed-RBE model using paired DeLong 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;Seventeen patients (15.0%) developed RIHT after a median follow-up of 25 months (range, 9–80 months). Under the fixed-RBE framework, the final bootstrap-selected predictors were complementary DLVH metrics: CD10L2 (cc), denoting the absolute volume not simultaneously receiving dose ≥ 10 Gy(RBE) and LETd ≥ 2 keV/µm, CD35L3 (cc), and CD5L4 (cc) for the thyroid gland, ipsilateral lobe, and contralateral lobe, respectively. The corresponding apparent AUCs were 0.794, 0.763, and 0.817, with optimism-corrected AUCs of 0.738, 0.708, and 0.766, respectively. Among the LET-dependent RBE models, the Wedenberg model based on contralateral lobe CD10L4 (cc) yielded the numerically highest optimism-corrected AUC of 0.772. Although LET-dependent RBE models showed slightly higher apparent AUCs than the fixed-RBE model in some analyses, paired DeLong tests did not demonstrate statistically significant differences.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;sectio","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187154","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":"Convolutional neural network metal artifact reduction in ultra-high resolution photon counting detector CT of middle- and inner-ear implants","authors":"Kevin J. Treb,&nbsp;Maryam Sadeghian,&nbsp;Wenchao Cao,&nbsp;Paul J. Farnsworth,&nbsp;John I. Lane,&nbsp;John C. Benson,&nbsp;Christopher P. Favazza,&nbsp;Andrea Ferrero,&nbsp;Andrew D. Missert,&nbsp;Matthew L. Carlson,&nbsp;Shuai Leng","doi":"10.1002/mp.70711","DOIUrl":"10.1002/mp.70711","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;Metallic inner- and middle-ear implants with submillimeter structures requiring ultra-high resolution (UHR) imaging invariably result in metal artifacts at CT, which can obscure adjacent microstructures and subtle pathologic findings. Current metal artifact reduction techniques can be less effective for small metal objects or metal in heterogeneous background anatomy, and can sometimes introduce new artifacts or noise.&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 and evaluate an image-domain convolutional neural network to reduce metal artifacts near inner- and middle-ear implants in UHR photon-counting-detector (PCD)-CT.&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;For network training, three stapes prosthesis models containing titanium and platinum were randomly oriented within a head-sized phantom and imaged on a PCD-CT with a UHR protocol. Metal and metal+artifact images were isolated by subtraction of the phantom background followed by CT number thresholding. Isolated artifact signal was enhanced via multiplication with a randomized factor ranging from one to a pre-determined artifact amplification scalar hyperparameter. Paired training inputs (with artifacts) and labels (without artifacts) were created by adding metal-free patient temporal bone images with metal+artifact and metal images, respectively. Combinations of 9 patient CT volumes and 24 metal CT volumes were used to create 216 CT volumes for training and validation with an 80%/20% training/validation split. The network was retrained with different amplification scalars (5, 10, 20, 30, 40) and with either mean-squared-error or mean-absolute-error loss functions to determine the optimal combination of these hyperparameters. The network was tested on 7 CT volumes from patients with three different implant types: stapes prostheses, cochlear implants, and a transverse sinus stent. Network performance was quantified through the artifact index, noise magnitude, CT number preservation of metal and bone, and dimensions of metal measured as the full-width at half-maximum of CT number line profiles.&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;Networks trained with mean-squared-error demonstrated a trade-off between metal artifact reduction and preservation of bone CT number as the artifact amplification scalar changed. The trade-off was not observed when mean-absolute error was used. Using the network with hyperparameters that best preserved bone and reduced artifacts simultaneously (mean-absolute-error, amplification scalar 20), the artifact index was improved by 83.2% (&lt;i&gt;p &lt;/i&gt;&lt; 0.001) across all implant types, and i","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187131","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 two-stage framework for fast proton spot map generation in pencil beam scanning prostate SBRT planning","authors":"Xueyan Tang,&nbsp;Hok Wan Chan Tseung,&nbsp;Mark Pepin,&nbsp;Jiasen Ma,&nbsp;David M. Routman,&nbsp;Doug J. Moseley,&nbsp;Brandon Reber,&nbsp;Jed E. Johnson,&nbsp;Jing Qian","doi":"10.1002/mp.70693","DOIUrl":"10.1002/mp.70693","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 pencil beam scanning (PBS) proton therapy, treatment plans are encoded as proton spot maps (PSMs). Although deep-learning methods can rapidly predict three-dimensional (3D) dose distributions from patient anatomy and planning intent, there is still no widely adopted direct, non-iterative approach to convert doses into physically deliverable spot patterns. This limits end-to-end automation in treatment planning and adaptive replanning for PBS.&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 developed and retrospectively evaluated GenSpot, a two-stage framework that reconstructs a PBS PSM from CT and dose. This study focused on the dose-to-spots reconstruction step under controlled, achievable-dose conditions, using clinical Monte Carlo (MC) dose distributions as input in a single-institution prostate stereotactic body radiation therapy (SBRT) cohort.&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;GenSpot uses a physics-informed projected proton spot map (PrPSM) representation that projects individual proton spots through the CT using water-equivalent thickness and percentage depth dose (PDD) information, aligning the spots with the CT and dose voxel grid while preserving a linear relationship with spot weights. Our dataset comprised 1036 fields from 259 prostate SBRT plans, split into training, validation, and test sets in an 80%/10%/10% ratio. A 3D SwinUNETR model was trained to predict PrPSMs from CT and dose inputs. Field-specific PSMs were then reconstructed from the predicted PrPSMs using column-wise nonnegative Lasso regression with precomputed PDD curves. MC dose distributions from GenSpot and clinical PSMs were compared using voxel-wise mean absolute error (MAE), 3D gamma analysis (3%/3 mm, 10% low-dose threshold), and dose-volume histogram (DVH) metrics at the composite-plan level.&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;Within the held-out test dataset, the selected SwinUNETR model achieved a mean PrPSM MAE of 0.06 ± 0.02 (normalized units) with high structural similarity to projected clinical PrPSMs. At the dose level, MC doses from GenSpot PSMs showed low MAE (0.07 ± 0.03 Gy in the nonzero-dose region) and high mean gamma passing rates (0.92 ± 0.05 at the field level and 0.99 ± 0.02 at the plan level). Composite-plan DVH metrics differences were generally within 1 Gy for targets and organs at risk, although the high-dose tail of the clinical target volume showed a modest systematic increase. Spot map complexity, measured by the number of energy layers and spot counts, was similar to that o","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139926","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":"Simultaneous T1, T2, R2*, PDFF, and triglyceride quantification in the head and neck using magnetic resonance fingerprinting on a 1.5T MR-Linac","authors":"Lucas McCullum,&nbsp;Ergys Subashi,&nbsp;Samuel L. Mulder,&nbsp;Brian A. Taylor,&nbsp;Ken-Pin Hwang,&nbsp;Clifton D. Fuller,&nbsp;Jason Ostenson","doi":"10.1002/mp.70670","DOIUrl":"10.1002/mp.70670","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The extraction of quantitative biomarkers on the MR-Linac, which have been shown to be promising in the head and neck, remains a significant challenge due to the strict time constraints.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study was to assess the technical feasibility of magnetic resonance fingerprinting (MRF) to simultaneously extract quantitative T1, T2, R2*, PDFF, and triglyceride composition in the head and neck on the MR-Linac.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Inversion-prepared, multi-echo, golden angle radial, variable flip angle, MRF data were acquired on a 1.5T MR-Linac. The data were corrected for phase errors in the bipolar multi-echo acquisition followed by low rank reconstruction with total variation regularization. A dictionary of signals parameterized by T1/T2 was calculated with an extended phase graph algorithm. The singular images were constructed using a signal subspace from the dictionary and the first singular images were then jointly fit for R2* and PDFF using the off-resonance information from an independent B0 map. The complete set of B0/T2*-corrected fat-water separated water singular images were fitted for T1/T2. Triglyceride composition was computed using the multi-echo complex readout. Three phantoms and three healthy volunteers were assessed for accuracy, repeatability, and reproducibility against validated reference sequences across five days using a test-retest setup.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>All coefficients of determination were at least 0.9 against reference sequences in standard phantoms across all quantitative parameters. The repeatability and reproducibility averaged under 5% in phantoms and under 10% in healthy volunteers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The proposed MRF acquisition demonstrated the potential to replace the current time-intensive sequences on the 1.5T MR-Linac, thus increasing the adoption of specialized quantitative imaging biomarker approaches at high temporal density in the head and neck.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139875","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":"2026 Joint AAPM COMP Meeting & Exhibition July 19–22 Vancouver, BC","authors":"","doi":"10.1002/mp.70698","DOIUrl":"10.1002/mp.70698","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129752","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}