Medical physics最新文献

{"title":"Biodistribution and dosimetry of 89Zirconium-labeled microbiota transplants in the pig gut","authors":"Praveen Dassanayake, Diksha Diksha, Gabriel Varela-Mattatall, Qin Sun, Sarah C. Donnelly, Mojmir Suchy, Dianne Bartolome, Shaena Furlong, Lela Deans, Heather Biernaski, Yvonne Huston, R. Terry Thompson, Jeremy P. Burton, Gerald Moran, Neil Gelman, Frank S. Prato, Mike S. Kovacs, Jonathan D. Thiessen, Donna E. Goldhawk, James Schellenberg, Matthew S. Fox","doi":"10.1002/mp.18087","DOIUrl":"10.1002/mp.18087","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The gastrointestinal (GI) microbiota, composed of diverse microbial communities, is essential for physiological processes, including immune modulation. Strains such as <i>Escherichia coli</i> Nissle 1917 support gut health by reducing inflammation and resisting pathogens. Microbial therapies using such strains may restore GI balance and offer alternatives to antibiotics, whose overuse contributes to antibiotic resistance. However, effective treatment will require optimizing delivery and understanding microbial dissemination and engraftment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We developed a method to monitor microbial migration and GI permeability post-ingestion using hybrid PET/MRI. To simulate probiotic therapy, bacteria were radiolabeled with <sup>89</sup>Zr, encapsulated, and administered to pigs. Organ level and whole-body dosimetry was determined from the time activity curves recorded over 7 days post ingestion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We administered <sup>89</sup>Zr-labeled <i>Lactobacillus crispatus</i> ATCC33820 (Gram-positive) to six female Duroc pigs (weight = 33.3 ± 4.6 kg) and <i>E. coli</i> Nissle 1917 (Gram-negative). Scans were performed between 6 h and 7 days post-ingestion using a hybrid PET/MRI system. The mean administered dose was 74.7 ± 12.9 MBq. Whole-body PET scans were acquired simultaneously with MRI using a T<sub>2</sub>-weighted HASTE sequence. Images were processed using 3D-Slicer co-registering PET with MRI and semi-automated organ segmentation was performed. Gender-averaged human equivalent organ-level effective doses (ED) and whole body ED were calculated using OLINDA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>PET imaging showed <sup>89</sup>Zr-labeled <i>L. crispatus</i> and <i>E. coli</i> post-ingestion localized primarily within the GI tract before excretion within feces. The highest mean ED for <sup>89</sup>Zr-labeled <i>L. crispatus</i> and <i>E. coli</i> were in the distal colon (26.8 ± 4.9 µSv/MBq and 28.4 ± 7.9 µSv/MBq, respectively) and proximal colon (17.9 ± 3.7 µSv/MBq and 18.4 ± 5.1 µSv/MBq, respectively). EDs in other organs were low. Whole body ED were 60.5 ± 9.5 µSv/MBq (<i>L. crispatus</i>) and 66.7 ± 14.9 µSv/MBq (<i>E. coli</i>).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The whole-body ED for <i>L. crispatus</i> and <i>E. coli</i> is lower than reported values for ingested tracers, such as that from <sup>89</sup>Zr l","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935353","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":"Cover","authors":"","doi":"10.1002/mp.17216","DOIUrl":"10.1002/mp.17216","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 8","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935062","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":"Pixel-level transformer GAN for enhanced parametric mapping of DCE MRI analysis","authors":"Yuxi Jin,&nbsp;Gengjia Lin,&nbsp;Qian Yang,&nbsp;Zixiang Chen,&nbsp;Haizhou Liu,&nbsp;Baijie Wang,&nbsp;Na Zhang,&nbsp;Hairong Zheng,&nbsp;Dong Liang,&nbsp;Dehong Luo,&nbsp;Zhou Liu,&nbsp;Peng Cao,&nbsp;Zhanli Hu","doi":"10.1002/mp.18092","DOIUrl":"10.1002/mp.18092","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and monitoring of cancers, as it reveals physiological and vascular characteristics of tumors. Traditional pharmacokinetic modeling necessitates high temporal resolution, resulting in relatively low signal-to-noise ratio (SNR) and spatial resolution with limited allocated time for each phase.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To explore the feasibility of using deep learning with sparse DCE MRI phases to generate dense temporal resolution DCE-MRI-derived parametric map.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An innovative approach, the vision transformer Pix2Pix generative adversarial network (VP-GAN), was introduced to translate the sparse DCE-MRI series into dense-phase DCE-MRI-based parametric maps, specifically targeting K^trans and v_e. The strengths of both Vision Transformers and GANs were utilized to capture complex temporal dynamics and spatial features. The proposed method was comprehensively compared with several existing deep learning models, both for the entire image and within regions of interest (ROI). Metrics used for comparison included Peak-Signal-to-Noise-Ratio (PSNR), Structural Similarity Index (SSIM), Pearson correlation analysis, and Bland-Altman analysis. Additionally, ROI histogram analysis was performed to assess the distribution of parametric values.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The parametric maps generated by the proposed approach were qualitatively and quantitatively consistent with the reference images. The performance of the comparative studies evidenced the superiority of VP-GAN over other approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The proposed model performs well in converting DCE-MRI with a subset of uniformly spaced time points into physiological parametric maps derived from dense-phase DCE-MRI, allowing for DCE-MRI analysis with much fewer phases.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927414","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":"Validation of a Monte Carlo model of a large-format kV flat-panel system","authors":"Nicholas Lowther,&nbsp;Marios Myronakis,&nbsp;Thomas Harris,&nbsp;Ross Berbeco,&nbsp;Matt Jacobson,&nbsp;Roshanak Etemadpour,&nbsp;Dianne Ferguson,&nbsp;Rony Fueglistaller,&nbsp;Pablo Corral Arroyo,&nbsp;Vera Birrer,&nbsp;Raphael Bruegger,&nbsp;Daniel Morf,&nbsp;Mathias Lehmann,&nbsp;Yue-Houng Hu","doi":"10.1002/mp.18093","DOIUrl":"10.1002/mp.18093","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;Online adaptive radiation therapy (ART) offers a paradigm shift in radiotherapy by enabling adjustments to the planned dose based on daily anatomical variation. In the context of cone-beam computed tomography (CBCT) for online ART on a standard linac, thoracic and abdominal treatment sites in particular present unique challenges due to the typically large treatment volumes, mobile anatomy, scatter-induced image quality degradation, and hounsfield unit (HU) limitations. A recent hardware and software upgrade for a standard linac, Varian TrueBeam (TB) v4.1 HyperSight, seeks to overcome these challenges through implementation of a larger kV imager panel (i.e., 43 × 43 cm), increased gantry speed (i.e., from 6 to 9°/s), and improved HU accuracy. However, investigation of the new upgrade is essential to harness the full potential of these advancements.&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 report on physical characterization and a digital Monte Carlo (MC) model of the new imaging system hardware.&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 open-source GEANT4 Application for Tomographic Emission (GATE) MC toolkit, which allows scintillation systems, including CBCT, to be accurately modeled, was utilized. All physical components of the new TB upgrade were modeled from vendor-provided geometry and material specifications. The model was validated using physical measurements acquired on the upgraded system. Specifically, the modulation transfer function (MTF), noise power spectrum (NPS), profiles across the physically larger detector, scatter-to-primary ratio (SPR), and loss in spatial resolution as a function of the increased gantry speed and an object's distance from isocenter. The latter was quantified using the pixel distance between the 15% and 85% intensities of the over-sampled edge spread function (ESF) for source-to-edge-phantom distances (SEPDs) of 80, 100, and 120 cm. Focal spot motion was also characterized by the MTF at SEPD of 100 cm.&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 MTF&lt;sub&gt;50&lt;/sub&gt; was 0.901 and 0.889 mm&lt;sup&gt;−1&lt;/sup&gt; for the measurement and simulation, respectively, for a 125 kVp beam. The normalized root mean square error (nRMSE) was 0.013. While small, the model displayed degraded spatial resolution accuracy for other beam qualities. The general trend of the physically measured normalized noise power spectrum (nNPS) curves was reproduced with the model at all beam energies; however, a small systematic offset was observed. Excellent agreement was observed between central-image x- and y-profiles of measured and MC-generated projections, indicating corr","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927531","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":"Localized hemodynamic assessment and rupture risk evaluation of intracranial aneurysms using the TESLA framework via computational fluid dynamics","authors":"Sajid Ali,&nbsp;Zhen-Ye Chen,&nbsp;Te-Chang Wu,&nbsp;Wei-Chien Huang,&nbsp;Tzu-Ching Shih","doi":"10.1002/mp.18071","DOIUrl":"10.1002/mp.18071","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;Intracranial aneurysms, particularly saccular types, are localized dilations of cerebral vessels prone to rupture, leading to life-threatening complications such as subarachnoid hemorrhage.&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 characterize the localized hemodynamic environment within the aneurysm dome and evaluate how spatial interactions among key flow parameters contribute to rupture risk, using a synergistic analytical framework.&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;We applied the targeted evaluation of synergistic links in aneurysms (TESLA) framework to analyze 18 intracranial aneurysms from 15 patients. Patient-specific vascular geometries were reconstructed from high-resolution three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA), acquired using a 1.5T magnetic resonance imaging (MRI) scanner (MAGNETOM Aera, Siemens Healthineers) with a 20-channel head and neck coil. TOF-MRA employed a gradient-echo sequence leveraging the inflow effect to enhance signal intensity from flowing blood, obviating the need for contrast agents. Imaging parameters were: TR/TE = 24/7 ms, flip angle = 22°, field of view (FOV) = 230 × 200 mm&lt;sup&gt;2&lt;/sup&gt;, matrix size = 320 × 196, 100 contiguous slices with a slice thickness of 0.7 mm, and voxel dimensions = 0.72 × 1.02 × 0.7 mm&lt;sup&gt;3&lt;/sup&gt; (acquired) and 0.7 × 0.7 × 0.7 mm&lt;sup&gt;3&lt;/sup&gt; (reconstructed isotropic). Computational fluid dynamics (CFD) simulations were performed to wall shear stress (WSS), time-averaged WSS (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), pressure gradient (PG), and vorticity. A standardized pulsatile inflow waveform (mean flow rate: 275 mL min&lt;sup&gt;−1&lt;/sup&gt;) was applied uniformly at the inlet of each model. Outflow boundary conditions assumed constant pressure at distal locations, with resistance equalization via extension segments. Hemodynamic parameters were compared between ruptured and unruptured aneurysms.&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 CFD analysis of 18 intracranial aneurysms revealed marked hemodynamic heterogeneity within the aneurysm dome, with WSS ranging from an average of 0.7042 Pa in low-stress zones associated with stagnant flow to peaks of 54.0371 Pa in high-stress regions indicative of mechanical strain, while TAWSS averaged 12.4875 Pa with maximum values reaching 25.9159 Pa, highlighting localized stress amplifications. Vorticity averaged 2,422.34 s&lt;sup&gt;−1&lt;/sup&gt; with peaks up to 4,645.50 s&lt;sup&gt;−1&lt;/sup&gt;, reflecting turbulent and recirculating flow, complemented by an OSI averaging 0.4557 and peaking at 0.4952, and R","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927439","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":"Uncertainty-guided cross-level fusion network for retinal OCT image segmentation","authors":"Jiaxin Wang,&nbsp;Weifang Zhu,&nbsp;Dehui Xiang,&nbsp;Xinjian Chen,&nbsp;Tao Peng,&nbsp;Qing Peng,&nbsp;Meng Wang,&nbsp;Fei Shi","doi":"10.1002/mp.18102","DOIUrl":"10.1002/mp.18102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Deep learning-based segmentation methods for optical coherence tomography (OCT) have demonstrated outstanding performance. However, the stochastic distribution of training data and the inherent limitations of deep neural networks introduce uncertainty into the segmentation process. Accurately estimating this uncertainty is essential for generating reliable confidence assessments and improving model predictions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To address these challenges, we propose a novel uncertainty-guided cross-layer fusion network (UGCFNet) for retinal OCT segmentation. UGCFNet integrates uncertainty quantification into the training process of deep neural networks and leverages this uncertainty to enhance segmentation accuracy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Our model employs an encoder–decoder architecture that quantitatively assesses uncertainty at multiple stages, directing the network's focus toward regions with higher uncertainty. By facilitating cross-layer feature fusion, UGCFNet enhances the comprehensive understanding of both semantic information and morphological details. Additionally, we incorporate an improved Bayesian neural network loss function alongside an uncertainty-aware loss function, enabling the network to effectively utilize these mechanisms for better uncertainty modeling.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We conducted extensive experiments on the publicly available AI-Challenger and OIMHS OCT segmentation datasets. The training, validation, and testing sets of the AI-Challenger dataset are comprised of 32, 8, and 43 OCT volumes, yielding a total of 4096, 1024, and 5504 B-scans, respectively. The training, validation, and testing sets of the OIMHS dataset consist of 100, 25, and 25 OCT volumes, resulting in 2,310, 798, and 751 B-scans, respectively. The results demonstrate that UGCFNet achieves state-of-the-art performance, with average Dice similarity coefficients of 79.47% and 93.22% on the respective datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our proposed UGCFNet significantly advances retinal OCT segmentation by integrating uncertainty guidance and cross-level feature fusion, offering more reliable and accurate segmentation outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927355","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 quantitative ionizing radiation acoustic imaging (iRAI) for radiation dose measurement: Validation from simulations to experiments","authors":"Yaocai Huang,&nbsp;Lise Wei,&nbsp;Dale Litzenberg,&nbsp;Borui Li,&nbsp;Chenshuo Ma,&nbsp;Hyeonwoo Kim,&nbsp;Yiming Liu,&nbsp;Claire Zhang,&nbsp;Paul L. Carson,&nbsp;Issam El Naqa,&nbsp;Wei Zhang,&nbsp;Xueding Wang","doi":"10.1002/mp.18091","DOIUrl":"10.1002/mp.18091","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 clinical radiation therapy (RT), accurately quantifying the delivered radiation dose to the targeted tumors and surrounding tissues is essential for evaluating treatment outcomes. Ionizing radiation acoustic imaging (iRAI), a novel passive and non-invasive imaging technique, has the potential to provide real-time in vivo radiation dose mapping during RT. However, current iRAI technology does not account for spatial variations in the detection sensitivity of the ultrasound transducer used to capture the iRAI signals, leading to significant errors in dose mapping.&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 paper presents the first detection sensitivity-compensated quantitative iRAI approach for measuring deposited radiation dose, aiming at improving dose mapping accuracy.&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;Detection sensitivity maps for the 2D matrix array transducer (MAT) were generated through both computational studies and experimental measurements. First, the Field II MATLAB toolbox was used to simulate the acoustic fields generated by the 2D MAT at various focal angles in the region of interest. Second, the prototype 2D MAT was applied to experimentally measure the acoustic signals generated by pulsed laser point sources distributed throughout the same volume as in the simulation. Then, in vitro experiments were conducted using homogeneous soft-tissue phantoms, where x-ray beams with square fields and a C-shaped treatment plan were separately delivered via a clinical linear accelerator (LINAC). Additionally, the propagation of acoustic waves induced by the x-ray beams with square fields was simulated using the K-Wave MATLAB toolbox. Correction factors derived from both the simulated and experimental sensitivity maps were applied to compensate for sensitivity-induced discrepancies in the iRAI reconstruction results. Dose distributions in uncompensated and sensitivity-compensated iRAI volumetric images were compared across various beam positions and field sizes. The agreement between the iRAI images and the treatment plan was quantitatively evaluated using structural similarity index measure (SSIM) and gamma index analysis.&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 experimental results, including the detection sensitivity map and iRAI measurements of x-ray beams with square fields, showed strong agreement with the corresponding simulated outcomes. Following compensation, the relative amplitudes of all iRAI images for beams targeting different positions converged toward 1. The co","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927361","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":"Nonstop gated CBCT for respiratory gating lung SBRT: A feasibility study","authors":"Mitchell Yu,&nbsp;Sean Berry,&nbsp;Yabo Fu,&nbsp;Wendy Harris,&nbsp;Weixing Cai,&nbsp;Michael Ziegenfus,&nbsp;Huiqiao Xie,&nbsp;Adam Wang,&nbsp;Daphna Gelblum,&nbsp;Boris Mueller,&nbsp;Laura Cervino,&nbsp;Tianfang Li,&nbsp;Xiang Li,&nbsp;Jean Moran,&nbsp;Hao Zhang","doi":"10.1002/mp.18084","DOIUrl":"10.1002/mp.18084","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;Free-breathing gated cone-beam computed tomography (gCBCT), which captures a specific anatomy coinciding with a preset gating window in the breathing cycle, is routinely prescribed to gating lung SBRT patients for pretreatment setup verification. However, a half-fan gCBCT scan can take 2–8 min (for a typical gating duty cycle of 30%–60% and patient breathing period of 3–6 s) on a C-arm linear accelerator because the gantry movement is interrupted and resumed by the respiratory gating signal multiple times over the scan. The long scan time increases patient on-table time, leading to discomfort and a higher likelihood of patient movement. Meanwhile, extra kV projections are acquired while the gantry is accelerating for the gCBCT scan, resulting in a higher imaging dose compared to 3D CBCT.&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 investigate the feasibility of a novel imaging paradigm named “nonstop gated CBCT (ngCBCT)” that improves upon current clinical gCBCT by substantially reducing the scan time and imaging dose while retaining high-quality 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;The ngCBCT is implemented by allowing the gantry to rotate continuously, with the kV x-ray beam activated only when the breathing signal falls within the preset gating window. Raw gCBCT projections of two gating lung SBRT patients were retrospectively retrieved and intentionally sampled based on each patient's breathing cycle to emulate the ngCBCT acquisitions. The datasets include both half-fan and full-fan acquisitions, representing the primary clinical scan geometries. Three reconstruction algorithms—Feldkamp–Davis–Kress (FDK), penalized likelihood iterative reconstruction (PL), and prior-image-based iterative reconstruction (PIBR)—were applied to these ngCBCT emulations to evaluate reconstruction performances on the non-uniform and under-sampled projections resulting from this acquisition strategy.&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 FDK reconstructions of ngCBCT are degraded with streak artifacts and have insufficient quality for clinical use. While PL yields improved reconstructions over FDK, the PIBR method consistently delivers the best visual and quantitative results with the aid of patient-specific prior images.&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;The proposed ngCBCT technique addresses the key limitations of current clinical gCBCT by substantially reducing data acquisition time and imaging dose. The ngCBCT with PIBR achieves adequate image quality and offers a promising oppo","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927362","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":"Scanned particle-beam tracking with beam correction based on predictive volumetric imaging: A simulation study","authors":"Takahisa Osanai,&nbsp;Seishin Takao,&nbsp;Kohei Yokokawa,&nbsp;Ye Chen,&nbsp;Taeko Matsuura,&nbsp;Keiji Kobashi,&nbsp;Norio Katoh,&nbsp;Takayuki Hashimoto,&nbsp;Hidefumi Aoyama,&nbsp;Naoki Miyamoto","doi":"10.1002/mp.18096","DOIUrl":"10.1002/mp.18096","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;Tracking irradiation to moving targets in spot-scanning particle therapy, which corrects the spot position and energy in real-time, may decrease treatment time and increase accuracy. However, because of the temporal performance of the system, clinical translation remains challenging. Processing time, including image acquisition, volumetric image synthesis, correction assessment, and system response, is required to control the actual treatment system. These processing delays cause millimeter-order discrepancies due to tumor motion. Predicting future states may compensate for this latency. However, research on predicting volumetric images required for energy correction assessment has not been reported.&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 investigate the dosimetric effectiveness of particle-beam tracking irradiation according to predictive volumetric imaging under various latency conditions.&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;Surrogate-driven volumetric image synthesis is combined with surrogate position prediction in the predictive volumetric imaging technique. A linear regression model in volumetric imaging that can derive internal deformation from surrogate displacement is established for each voxel from a four-dimensional computed tomography (4DCT) dataset in the modeling process. A volumetric image is predictively synthesized during the imaging process using the surrogate position predicted by a pretrained long short-term memory network. This predictively synthesized image enables the prospective assessment of beam parameter correction, including spot position and energy. In this study, 4DCT datasets and time-series trajectory data of the internal marker from three patients each with lung, liver, and pancreatic cancers were utilized for the dosimetric simulation. An intensity-modulated proton therapy plan was generated for each patient. Dosimetric simulations were conducted assuming the latencies of 133.3, 266.6, and 400.0 ms. Assessments included (1) tracking irradiation without latency as a benchmark, (2) tracking irradiation with latency but without prediction, and (3) tracking irradiation with latency and prediction. Further, dose–volume histograms and dose metrics of the clinical target volume (CTV) were compared.&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;Doses in tracking with prediction were comparable to those in the benchmark. Differences in D99%, D95%, and D5% of the CTV in the lungs between the treatment plan and tracking irradiation without prediction exceeded 5% at all latencies. Differences in D95% and D5% in tracking irradiation w","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927360","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":"Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization","authors":"Niloofar Ziasaeedi,&nbsp;Yannick Lemaréchal,&nbsp;Mohsen Agharazii,&nbsp;Venkata S. K. Manem,&nbsp;Philippe Després,&nbsp;Leyla Ebrahimpour","doi":"10.1002/mp.18070","DOIUrl":"10.1002/mp.18070","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The increased use of CT imaging has elevated the incidental detection of renal masses, necessitating accurate differentiation between benign and malignant nodules. Radiomics offers potential for improved diagnostics; however, it is limited by variability in imaging parameters such as slice thickness, highlighting the need for effective harmonization techniques.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study is to conduct a comprehensive radiomics analysis, evaluating the impact of slice thickness in distinguishing between kidney cysts and tumors using machine learning techniques, thus contributing to more precise and effective patient management strategies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We utilized a publicly available dataset, KITS23, and extracted radiomic features from contrast-enhanced computed tomography (CT) scans using the PyRadiomics library. The dataset consists of 599 cases, which were divided into training (60%) and testing (40%) cohorts to develop and validate predictive models. Six feature selection methods and ten machine learning classifiers were employed. Additionally, the Nested Combat harmonization technique was applied to address variations in imaging protocols across institutions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We observed improvements in AUC values across various feature selection methods and classifiers after harmonization, with the highest AUC reaching 0.95. This represents significant enhancements in model performance, with mean AUC improvements ranging from 0.7% to 7.7% across different feature selection methods, bringing our results in line with, and in some cases surpassing, the AUCs reported in the literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings underscore the potential of radiomics-based machine learning models to enchance diagnostic accuracy and patient management in clinical practice. The use of harmonization techniques, such as, Nested Combat is crucial in achieving reliable and generalizable predictive models for renal oncology.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927386","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}