Medical physics最新文献

{"title":"A viscous fluid model for large-scale motion estimation in image-guided radiotherapy","authors":"Tom J. W. Draper, Cornel Zachiu, Bas W. Raaymakers","doi":"10.1002/mp.17967","DOIUrl":"https://doi.org/10.1002/mp.17967","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Image-guided radiotherapy (IGRT) is often hampered by geometric inaccuracies introduced by anatomical and physiological motion. Although a wide array of deformable image registration (DIR) solutions have been proposed toward motion estimation and compensation during IGRT, their accuracy and precision generally deteriorates within areas showcasing particularly large displacements such as the thorax, abdomen and pelvis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>In this work, we propose a physics-derived DIR algorithm for motion estimation during IGRT, designed to be specifically suitable for highly deforming anatomical areas. The proposed solution also has a single configuration parameter controlling the volumetric deformations of the anatomy and a high computational performance, lending it particularly compatible with online adaptive workflows.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We hereby address the DIR problem by modeling anatomical motion as the flow of a viscous fluid. In this context, we solve a simplified form of the Navier–Stokes equations where the dissimilarity between the registered images plays the role of actuating force. The high degree-of-freedom of the solutions resulting from viscous fluid dynamics thereby allows for the estimation of large-scale deformations. For computational purposes, a highly parallelizable FFT-based numerical solver was used, allowing for its seamless implementation on graphical processing units. The Jacobian determinant was used to analyze tissue compression and expansion at a voxel-wise level and to identify implausible deformations. The accuracy and precision of the proposed algorithm was analyzed for thoracic CT and pelvic MR images showcasing particularly large deformations. A gold standard consisting of annotated landmarks was available for the CT images, while for the MR data manually-approved contours were generated using a semi-automatic procedure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed DIR solution showcased an overall accuracy of 1 – 2 mm for the thoracic CT data and a Dice similarity coefficient of 0.8 – 0.9 for the contours defined on the MR images. Moreover, the model estimates motion with a smooth distribution of the Jacobian determinant. The average computational latency ranged between 20 s to 3.5 min, dependent on the size of the registered images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In this work, we have fo","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634965","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":"Estimates of cardiac implanted electronic device neutron dose for a pencil beam scanning proton therapy system","authors":"Karolyn M. Hopfensperger,&nbsp;Wangyao Li,&nbsp;Seth H. Sheldon,&nbsp;Ronny L. Rotondo,&nbsp;Ronald C. Chen,&nbsp;Sara St. James,&nbsp;Yuting Lin","doi":"10.1002/mp.17987","DOIUrl":"https://doi.org/10.1002/mp.17987","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The presence of a cardiovascular implantable electronic device (CIED) is frequently viewed as a contraindication to proton therapy due to the creation of secondary neutrons that can potentially damage CIED electronics. As a result, photon therapy is typically recommended for patients with CIEDs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The study aims to provide a method for estimating neutron dose to a CIED by measuring equivalent neutron dose at varying distances from isocenter and field edge. This estimation can be used to guide clinical decisions by balancing the risk of neutron-induced CIED damage against the therapeutic benefits of proton therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Standardized three-dimensional measurement fields of varying dimensions were delivered using the IBA ProteusONE (Ion Beam Applications SA, Walloon Brabant, Belgium) pencil beam scanning proton therapy system, with each field delivering an RBE-weighted dose of 2 Gy. Baseline measurement included one treatment field being delivered with a range shifter. BD-PND detectors (Bubble Technology Industries, Chalk River, ON) were placed at a defined distances from the surface mark of beam isocenter and field edge to record neutron doses. Additionally, clinical treatment plans including two prostate/pelvis plans, two head &amp; neck plans, two brain plans, and one breast plans were delivered, with detectors placed at positions corresponding to the location of a CIED.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For fields without a range shifter, the measured neutron dose ranged from 0.11 µSv/2 Gy for the smallest field at 50 cm from isocenter to 11.0 µSv/2 Gy for the largest field size at 10 cm from isocenter for fields. The addition of a range shifter to the 10 × 10 × 10 cm³ field increased the dose to 0.66 µSv/2 Gy at 50 cm to 14.2 µSv/2 Gy at 10 cm from isocenter. For clinical treatment plans, neutron doses ranging from 0.14 µSv/2 Gy to 9.5 µSv /2 Gy at 24–56 cm from isocenter.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>These measurements provide a foundation for estimating neutron doses to CIEDs, enabling physicists and physicians to evaluate the feasibility of proton therapy for patients with CIEDs. The results support informed clinical decision-making by quantifying the risk of neutron-induced damage relative to the therapeutic benefits of proton therapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635061","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 Monte Carlo simulation framework for investigating the effect of inter-track coupling on \u0000 \u0000 \u0000 \u0000 H\u0000 2\u0000 \u0000 \u0000 O\u0000 2\u0000 \u0000 \u0000 ${rm H}_{2}{rm O}_{2}$\u0000 productions at ultra-high dose rates","authors":"Ramin Abolfath,&nbsp;Sedigheh Fardirad,&nbsp;Houda Kacem,&nbsp;Marie-Catherine Vozenin,&nbsp;Abbas Ghasemizad","doi":"10.1002/mp.17972","DOIUrl":"https://doi.org/10.1002/mp.17972","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;Lower production of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm H}_{2}{rm O}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; in water is a hallmark of ultra-high dose rate (UHDR) compared to the conventional dose rate (CDR). However, the current computational models based on the predicted yield of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm H}_{2}{rm O}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; are the opposite of the experimental data.&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 present a multi-scale formalism to reconcile the theoretical modeling and the experimental observations of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm H}_{2}{rm O}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; production and provide a mechanism for the suppression of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm H}_{2}{rm O}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; at FLASH-UHDR.&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;An analytical model was constructed for the rate equation in the production of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635387","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":"Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound","authors":"Roni Yoeli-Bik,&nbsp;Heather M. Whitney,&nbsp;Hui Li,&nbsp;Agnes Bilecz,&nbsp;Jacques S. Abramowicz,&nbsp;Li Lan,&nbsp;Ryan E. Longman,&nbsp;Maryellen L. Giger,&nbsp;Ernst Lengyel","doi":"10.1002/mp.17983","DOIUrl":"https://doi.org/10.1002/mp.17983","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Adnexal masses are heterogeneous and have varied sonographic presentations, making them difficult to diagnose correctly.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Our study aimed to develop an innovative hybrid artificial intelligence/computer-aided diagnosis (AI/CADx)-based pipeline to distinguish between benign and malignant adnexal masses on ultrasound imaging based upon automatic segmentation and echogenic-based classification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The retrospective study was conducted on a consecutive dataset of patients with an adnexal mass. There was one image per mass. Mass borders were segmented from the background via a supervised U-net algorithm. Masses were spatially subdivided automatically into their hypo- and hyper-echogenic components by a physics-driven unsupervised clustering algorithm. The dataset was separated by patient into a training/validation set (95 masses; 70%) and an independent held-out test set (41 masses; 30%). Eight component-based radiomic features plus a binary measure of the presence or absence of solid components were used to train a linear discriminant analysis classifier to distinguish between malignant and benign masses. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, negative predictive value, positive predictive value, and accuracy at target 95% sensitivity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The cohort included 133 patients with 136 adnexal masses. In distinguishing between malignant and benign masses, the pipeline achieved an AUC of 0.90 [0.84, 0.95] on the training/validation set and 0.93 [0.83, 0.98] on the independent test set. Strong diagnostic performance was observed at the target 95% sensitivity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A novel hybrid AI/CADx echogenic components-based ultrasound imaging pipeline can distinguish between malignant and benign adnexal masses with strong diagnostic performance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635406","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":"Use of a deep learning neural network to generate bone suppressed images for markerless lung tumor tracking","authors":"Jason Luce,&nbsp;Mandeep Kaur,&nbsp;Joseph Dingillo,&nbsp;Andrew Keeler,&nbsp;Mathias Lehmann,&nbsp;Daniel Morf,&nbsp;Liangjia Zhu,&nbsp;Hyejoo Kang,&nbsp;Ha Nguyen,&nbsp;Michal Walczak,&nbsp;Matthew M. Harkenrider,&nbsp;John C. Roeske","doi":"10.1002/mp.17949","DOIUrl":"https://doi.org/10.1002/mp.17949","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;Markerless tumor tracking (MTT) is being considered for real-time motion management of lung tumors. However, bony structures in conventional x-ray images can obfuscate the tumor, increasing tracking difficulty. Bone suppression using dual energy subtraction (DES) can improve tumor visibility but requires additional hardware or software that is not currently available with commercial on-board imaging (OBI) systems.&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 compares DES images to synthetic DES (sDES) images generated by a U-net neural network, examining both image quality and tracking results.&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;High (120 kV) and low (60 kV) energy image pairs were obtained over 180-degree rotation using fast-kV switching for a motion phantom and 20 lung cancer patients. DES images were generated offline using weighted logarithmic subtraction. A U-net was then trained to transform 120 kV images into sDES images. Images from the phantom (2694 image pairs) and 20 patients (4499 image pairs), were divided into training, validation, and test sets consisting of 70%, 15%, and 15% of the images, and used for network training and evaluation. The similarity between sDES images and ground truth DES images were evaluated using histogram comparison, structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and the 2D correlation coefficient (2DCC). Separately, a template-tracking algorithm was used to predict tumor location on patient and phantom sDES images. Since there was no ground truth location for the patient images, the predicted locations of the tumor in the HE and sDES images were compared against the predicted locations in the DES images. For the phantom images, tracking success rate (TSR) was defined as the percentage of images in which the predicted and ground truth tumor location differed by &lt;2 mm, missing frames (MF) was defined as the percentage of images in which the tracking algorithm failed, and the mean absolute error (MAE) was also calculated from the differences between predicted and ground truth locations of the tumor.&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;Histogram count comparisons showed good agreement between the pixel distribution of sDES and DES images. Average SSIM, PSNR, and 2DCC scores for sDES images were 0.80 ± 0.05, 28.9 ± 3.4, and 0.97 ± 0.03 for phantom images, and 0.85 ± 0.04, 26.2 ± 3.5, and 0.97 ± 0.03 for patient images. For the patient images, the median tracking difference was 0.5 mm for HE versus DES images,","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624759","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":"ICD lead and primary metal artifact detection and inpainting in cardiac CT images","authors":"Trevor McKeown,&nbsp;H Michael Gach,&nbsp;Yao Hao,&nbsp;Hongyu An,&nbsp;Clifford G. Robinson,&nbsp;Phillip S. Cuculich,&nbsp;Deshan Yang","doi":"10.1002/mp.17947","DOIUrl":"https://doi.org/10.1002/mp.17947","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;Quantifying cardiac motion on pre-treatment CT imaging for stereotactic arrhythmia radiotherapy patients is difficult due to image artifacts caused by metal leads of implantable cardioverter-defibrillators (ICDs). The CT scanners’ onboard metal artifact reduction tool does not sufficiently reduce these artifacts. More advanced artifact reduction techniques require the raw CT projection data and thus do not apply to already reconstructed CT images. New methods are needed to accurately reduce the primary metal artifacts from ICD leads in already reconstructed CTs to recover the otherwise lost anatomical information.&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 methodology to automatically detect the ICD lead wires and surrounding primary metal artifacts in cardiac CT scans and inpaint the affected volume with anatomically consistent structures and values.&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;Breath-hold ECG-gated 4DCT scans of 12 patients who underwent cardiac radiation therapy for treating ventricular tachycardia were collected. The primary metal artifacts in the images caused by the ICD leads were manually contoured. A 2D U-Net deep learning (DL) model was developed to segment the metal artifacts automatically using eight patients for training, two for validation, and two for testing. A dataset of 592 synthetic CTs was prepared by adding segmented metal artifacts from the patient 4DCT images to artifact-free cardiac CTs of 148 patients. A 3D image inpainting DL model was trained to refill the metal artifact portion in the synthetic images with realistic image contents that approached the ground truth artifact-free images. The trained inpainting model was evaluated by analyzing the automated segmentation results of the four heart chambers with and without artifacts on the synthetic dataset. Additionally, the raw cardiac patient images with metal artifacts were processed using the inpainting model and the results of metal artifact reduction were qualitatively inspected.&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 artifact detection model worked well and produced a Dice score of 0.958 ± 0.008. The inpainting model for synthesized cases was able to recreate images nearly identical to the ground truth with a structural similarity index of 0.988 ± 0.012. With the chamber segmentations on the artifact-free images as the reference, the average surface Dice scores improved from 0.684 ± 0.247 to 0.964 ± 0.067 and the Hausdorff distance reduced from 3.4 ± 3.9 mm to 0.7 ± 0.7 mm. The inpainting model's use on cardiac patient CTs was visually inspected and the artifact-inpain","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624402","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":"Implicit neural prior-guided diffusion for spectral CT reconstruction","authors":"Yizhong Wang,&nbsp;Ningning Liang,&nbsp;Shaoyu Wang,&nbsp;Jie Guo,&nbsp;Xinrui Zhang,&nbsp;Zhizhong Zheng,&nbsp;Ailong Cai,&nbsp;Lei Li,&nbsp;Bin Yan","doi":"10.1002/mp.17946","DOIUrl":"https://doi.org/10.1002/mp.17946","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Spectral computed tomography (CT) plays a crucial role in various fields. However, the cumulative radiation dose from repeated x-ray CT examinations has raised concerns about potential health risks. Reducing the projection view is an effective strategy to reduce the radiation dose, but this will lead to a notable degradation in image quality, resulting in streaking artifacts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work aims to develop a novel spectral CT reconstruction method to alleviate the ill-posed nature of the sparse sampling image reconstruction, while suppressing streaking artifacts and recovering detailed structures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In the scope of this work, we propose an implicit neural representation (INR) prior-guided diffusion (NeRDiff) method for spectral CT reconstruction, effectively combining the capabilities of implicit prior representation of INR and detail recovery of score-based generative models (SGM). NeRDiff includes two key designed phases: gradient-penalized INR learning and Pos-INR guided SGM reconstruction. In the first phase, an improved INR is devised and utilized to enhance the network's ability of representing complex signals by applying the variable-periodic activation function in multilayer perception network and adopting a dual-domain loss function. In the second phase, the INR prior is incorporated as a prior guiding Langevin dynamics sampling in the reverse diffusion process of SGM. In addition, a unified mathematical model and an efficient algorithm are proposed to enhance reconstruction stability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Quantitative and qualitative assessments on ultra-sparse-view datasets from numerical simulation and preclinical mouse underscore the superiority of NeRDiff over alternative methods. Especially in the simulation experiment, the NeRDiff method achieves improvement of at least 4.75 and 1.70 dB in PSNR under 20-view compared to the SGM proposed by Song et al. (Song-CT) and the wavelet-improved score-based generative model (WSGM).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In this work, we propose the NeRDiff method for highly ill-defined spectral CT reconstruction tasks. We have conducted a series of experiments in the ultra-sparse-view reconstruction task, and the experimental results consistently demonstrate the remarkable capabilities of NeRDiff in terms of anti-artifact performance and detail preservation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624833","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":"Virtual imaging trials are paving the way for the future of medical imaging","authors":"Andrew D. A. Maidment,&nbsp;Liesbeth Vancoillie","doi":"10.1002/mp.17959","DOIUrl":"https://doi.org/10.1002/mp.17959","url":null,"abstract":"&lt;p&gt;Over the past 5 years, virtual imaging trials (VITs) have gained significant momentum, driving advancements in the field of medical imaging. While several laboratories have been developing VITs with specific foci for years, the establishment of the National Center for Virtual Imaging Trials by the NIH and other recent efforts has brought broader recognition and visibility to the discipline. Since then, VITs have evolved into a global initiative, with active programs emerging across Europe and beyond. This shift reflects not only a geographic expansion but a fundamental transformation in how imaging research is approached—providing a platform for collaboration, innovation, and international engagement.&lt;/p&gt;&lt;p&gt;The 2024 VITM conference exemplified the growing importance of the field. More than a traditional scientific meeting, it showcased the strength of a global, interdisciplinary community dedicated to shaping the future of medical imaging. The event attracted a broad spectrum of participants, including students, researchers, industry professionals, and regulatory representatives. This diversity fostered dynamic discussions and affirmed that VITs are poised to become foundational to the development and evaluation of imaging technologies. We are excited to see this movement grow into a true community, where collaboration and shared knowledge are at the forefront.&lt;/p&gt;&lt;p&gt;Looking ahead to VITM 2025 in Manchester, the momentum continues to build, demonstrating the increased interest in VITs. Submitted abstracts span a wide range of topics and disciplines, highlighting the versatility and broad appeal of VITs. Interest in the field was further evident at SPIE 2025, where the findings from VITM 2024 were presented. The overwhelmingly positive response highlights the essential role of VITs, which are shaping the future of medical imaging research through a dynamic, evolving community that continues to innovate.&lt;/p&gt;&lt;p&gt;At their core, VITs provide a controlled, repeatable, and ethical framework for evaluating imaging technologies. Rooted in physical principles, VITs enable systematic approaches to testing, protocol optimization, and safe evaluation of new technologies, which will better ensure patient safety and exam efficacy. As adoption grows, the field is attracting increasing attention from physicists, engineers, clinicians, and policymakers—each recognizing the role VITs can play in accelerating progress while maintaining safety and rigor.&lt;/p&gt;&lt;p&gt;What is most striking is the global reach of this movement. The 2024 conference brought together participants from around the world, confirming that VITs have moved beyond niche status to become a truly international phenomenon. This global collaboration, powered by interdisciplinary expertise, is essential to the continued evolution of the field. It is through this shared commitment and interdisciplinary collaboration that VITs will continue to grow and evolve, driving innovation and advancing the field of m","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17959","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634966","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":"Helical-like scan and upright CBCT imaging algorithms based on robotic-arm system","authors":"Tong Lin,&nbsp;Tianling Lyu,&nbsp;Jiashun Wang,&nbsp;Zhan Wu,&nbsp;Yan Xi,&nbsp;Dillenseger Jean-Louis,&nbsp;Wentao Zhu,&nbsp;Hao Tang,&nbsp;Shipeng Xie,&nbsp;Yang Chen","doi":"10.1002/mp.17997","DOIUrl":"https://doi.org/10.1002/mp.17997","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Upright position CT scans enable body imaging under realistic conditions and have been widely adopted in rehabilitation medicine. However, they face challenges such as limited acquisition angles, floor stability issues, and a restricted field of view (FoV) along the <i>Z</i>-axis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The objective of this study is to propose an algorithm that enables high-quality image reconstruction in lightweight robotic-arm cone-beam CT (CBCT) systems, addressing challenges related to mechanical vibrations, a limited <i>Z</i>-axis scanning range, and inhomogeneous sampling.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A long <i>Z</i>-axis helical steel ball phantom and a greedy ball-positioning algorithm are proposed to enhance geometrical calibration accuracy. A data completeness-driven method optimizes the scanning pitch for rapid full-body scans without significant image degradation. Additionally, a normalized projection-based FDK-style algorithm enhances reconstruction quality under reverse helical scanning constraints.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed upright reverse helical CBCT system demonstrated competitive reconstruction accuracy, achieving RMSE values of 0.0421 for Shepp–Logan, 0.3163 for foam-like, and 115.08 for VHP phantoms. Additionally, the proposed algorithm maintained computational efficiency, completing reconstructions in 45.3, 64.0, and 189.4 s, respectively, significantly outperforming iterative methods while preserving image quality. Furthermore, it significantly reduced radiation dose compared to conventional helical CT, achieving dose reductions from 460 to 50.7 mGy.cm for a 32 cm phantom and from 1050.0 to 112.6 mGy cm for a 16 cm water phantom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This work provides a robust solution for long-length <i>Z</i>-axis imaging in upright positions, as well as for unstable and nonstandard projection sampling. The proposed framework holds potential for advancing the use of robotic-arm upright CBCT systems in orthopedic functional evaluations and other clinical applications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635147","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":"Super-resolution CBCT on a new generation flat panel imager of a C-arm gantry linear accelerator","authors":"Licheng Kuo,&nbsp;Feifei Li,&nbsp;Yabo Fu,&nbsp;Hao Zhang,&nbsp;Laura A. Cervino,&nbsp;Jean M. Moran,&nbsp;Xiang Li,&nbsp;Tianfang Li","doi":"10.1002/mp.18000","DOIUrl":"https://doi.org/10.1002/mp.18000","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;Kilovoltage cone-beam computed tomography (kV CBCT) is vital for image-guided radiotherapy (IGRT). The new RTI4343iL panel on the Varian TrueBeam LINAC offers higher resolution but requires binning to achieve practical frame rates, leading to projection resolution loss. Existing super-resolution (SR) techniques have been applied to enhance CBCT image quality but primarily operate in the image domain, struggling to restore resolution loss in the projection domain.&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 feasibility of a deep learning (DL) SR model, based on a conditional Generative Adversarial Networks (cGANs) architecture, for enhancing the spatial resolution of CBCT acquired with the new RTI4343iL panel in the projection domain. We hypothesize that projection-domain deblurring will primarily depend on the detector and minimally on patient anatomy, enhancing primary signal resolution without significantly altering scatter distribution. The study quantitatively assessed the impact of SR-enhanced projections on the quality of reconstructed CBCT 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;A DLSR model was developed to enhance CBCT resolution in the projection domain.  For data acquisition, a Varian TrueBeam system equipped with the RTI4343iL panel was used, which features a native high-resolution image size of 2848 × 2144 pixels, but operates in 2 × 8 binning mode (1424 × 268 pixels) during CBCT scans to mitigate data readout speed limitations. Following thorax CBCT protocols, 576 pairs of CBCT projections were acquired at two resolutions using Rando, Longman, and Steeve phantoms. Of these, 460 pairs were allocated for model training, while 116 were reserved for validation. Model testing involved 144 Dynamic Thorax projections and CBCT reconstructions utilizing Catphan 604 phantoms. The DL SR model was built on a cGANs framework with a U-Net generator. Image enhancement was quantitatively evaluated with metrics including peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM), feature similarity index measure (FSIM), and mean absolute percentage error (MAPE).&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 DL SR model effectively enhanced image resolution, producing SR projections with greater detail and improved structural clarity. Quantitative analysis showed that the SR-enhanced projections outperformed upscaled low-resolution (LR) projections with higher PSNR (44.4 vs. 43.7, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), lower MSE (187,083.7 vs. 205,364.4, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), and improved MAPE (7.6% vs. 13.5%, &lt;i&gt;p&lt;/","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635367","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}