David Santiago Ayala Alvarez, Peter G. F. Watson, Marija Popovic, Veng Jean Heng, Michael D. C. Evans, Valerie Panet-Raymond, Jan Seuntjens
{"title":"Evaluation of the TG-43 formalism for intraoperative radiotherapy dosimetry in glioblastoma treatment","authors":"David Santiago Ayala Alvarez, Peter G. F. Watson, Marija Popovic, Veng Jean Heng, Michael D. C. Evans, Valerie Panet-Raymond, Jan Seuntjens","doi":"10.1002/mp.17930","DOIUrl":"https://doi.org/10.1002/mp.17930","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Intraoperative radiation therapy (IORT) using the INTRABEAM system has shown promise in glioblastoma treatment. However, accurate dosimetry remains challenging due to the low-energy photons used and the heterogeneity of tissues in the brain. Current clinical practice relies on the TARGIT method, but more robust approaches, including the TG-43 formalism and Monte Carlo (MC) simulations, warrant investigation for potential improvements in dose calculation accuracy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To evaluate the TG-43 dosimetry formalism for IORT dose calculations in glioblastoma treatment using the INTRABEAM system, comparing it with the TARGIT method and MC simulations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We analyzed the dose distributions in 20 patients from the INTRAGO trial. The TG-43 formalism was validated against MC simulations in water (<span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>MC</mi>\u0000 <mi>w</mi>\u0000 </msub>\u0000 <annotation>${rm MC}_{rm w}$</annotation>\u0000 </semantics></math>) using global/local dose differences and gamma analysis (1%/1mm). Organ at risk (OAR) doses were calculated using TG-43, TARGIT, <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>MC</mi>\u0000 <mi>w</mi>\u0000 </msub>\u0000 <annotation>${rm MC}_{rm w}$</annotation>\u0000 </semantics></math>, and MC in heterogeneous media (<span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>MC</mi>\u0000 <mi>het</mi>\u0000 </msub>\u0000 <annotation>${rm MC}_{rm het}$</annotation>\u0000 </semantics></math>). Combined IORT and external beam radiotherapy (EBRT) doses were evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>TG-43 showed good agreement with <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>MC</mi>\u0000 <mi>w</mi>\u0000 </msub>\u0000 <annotation>${rm MC}_{rm w}$</annotation>\u0000 </semantics></math>, with a 98.0% gamma pass rate. The mean global dose difference was 0.07% <span></span><math>\u0000 <semantics>\u0000 <mo>","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.17930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624398","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":"Denoising pediatric cardiac photon-counting CT data with sparse coding and data-adaptive, self-supervised deep learning","authors":"Darin P. Clark, Joseph Y. Cao, Cristian T. Badea","doi":"10.1002/mp.17918","DOIUrl":"https://doi.org/10.1002/mp.17918","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The judicious use of CT in pediatric cardiac applications is warranted because young patients face the need for repeated imaging and increased lifetime cancer risk after ionizing radiation exposure. The quality of pediatric cardiac CT scans is variable because of limited protocols optimizations for pediatric patients, the common presence of metallic implants following treatment, and disparities in denoising algorithm performance between adult and pediatric scans. Two recent technological developments promise to improve the average quality of pediatric CT scans at fixed or reduced dose: clinical photon-counting CT (PCCT) and deep learning (DL) algorithms for CT image denoising. Given advancements to accommodate variable image quality, these technologies will deliver improved spatial resolution, noise performance, and contrast resolution for pediatric cardiac CT imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To advance self-supervised DL denoising methods to accommodate variable image quality in pediatric cardiac CT data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Starting with the popular Vision Transformer (ViT) DL architecture, two targeted architectural changes were made: (1) the multi-layer perceptrons (MLPs) were modified to allow cross-token recombination of encoded image data following attention computations (parallels patch-wise weighting and averaging in non-local means [NLM]), and (2) the network head was replaced with the equivalent of an overcomplete dictionary to perform dictionary sparse coding (SC). This modified, 3D ViT (mViT) was then trained in a dynamic fashion: the balance between data fidelity and representation sparsity was adjusted during training such that the average fidelity error remained consistent with localized estimates of image noise. To demonstrate the newly proposed method, the mViT was trained with pediatric cardiac photon-counting x-ray CT data with variable levels of image noise (NAEOTOM Alpha PCCT scanner; retrospective data from 20 patients scanned at Duke University; ages: 1–18 years; iterative reconstruction noise level in the left ventricle: 20–55 HU). Data from one patient with the highest levels of noise was reserved for validation. Testing data included Alpha data from three additional Duke patients (2 < 1 year old) and a murine cardiac PCCT data set acquired on a preclinical system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The validation denoising results demonstrate that SC with the mViT preserves anatomic structures relevant to the diagnosis and treatment of congenital heart defects (coronary artery origins; valve leaflets; le","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":"144624767","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":"Joint enhancement of automatic chest x-ray diagnosis and radiological gaze prediction with multistage cooperative learning","authors":"Zirui Qiu, Hassan Rivaz, Yiming Xiao","doi":"10.1002/mp.17977","DOIUrl":"https://doi.org/10.1002/mp.17977","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>As visual inspection is an inherent process during radiological screening, the associated eye gaze data can provide valuable insights into relevant clinical decision processes and facilitate computer-assisted diagnosis. However, the relevant techniques are still under-explored.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>With deep learning becoming the state-of-the-art for computer-assisted diagnosis, integrating human behavior, such as eye gaze data, into these systems is instrumental to help guide machine predictions with clinical diagnostic criteria, thus enhancing the quality of automatic radiological diagnosis. In addition, the ability to predict a radiologist's gaze saliency from a clinical scan along with the automatic diagnostic result could be instrumental for the end users.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a novel deep learning framework for joint disease diagnosis and prediction of corresponding radiological gaze saliency maps for chest x-ray scans. Specifically, we introduce a new dual-encoder multitask UNet, which leverages both a DenseNet201 backbone and a Residual and Squeeze-and-Excitation block-based encoder to extract diverse features for visual saliency map prediction and a multiscale feature-fusion classifier to perform disease classification. To tackle the issue of asynchronous training schedules of individual tasks in multitask learning, we propose a multistage cooperative learning strategy, with contrastive learning for feature encoder pretraining to boost performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our proposed method is shown to significantly outperform existing techniques for chest radiography diagnosis (AUC = 0.93) and the quality of visual saliency map prediction (correlation coefficient = 0.58).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Benefiting from the proposed multitask, multistage cooperative learning, our technique demonstrates the benefit of integrating clinicians' eye gaze into radiological AI systems to boost performance and potentially explainability.</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.17977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634964","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}
Xiaoyu Hu, Yan Dai, Ahad Ollah Ezzati, Junghoon Lee, Jie Deng, Xun Jia
{"title":"Report on the quantitative intra-voxel incoherent motion diffusion MRI reconstruction grand challenge","authors":"Xiaoyu Hu, Yan Dai, Ahad Ollah Ezzati, Junghoon Lee, Jie Deng, Xun Jia","doi":"10.1002/mp.17998","DOIUrl":"https://doi.org/10.1002/mp.17998","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The 2024 quantitative intra-voxel incoherent motion diffusion MRI (IVIM-dMRI) reconstruction grand challenge aimed to benchmark and advance reconstruction algorithms for extracting quantitative tissue parameters from diffusion MRI (dMRI) data. Focusing on the IVIM model, the challenge aimed to improve the accuracy and robustness of clinical parameter estimation, addressing key barriers to broader clinical adoption.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Participants were tasked with reconstructing fractional perfusion, pseudo-diffusion coefficient, and true diffusion coefficient from simulated <span></span><math>\u0000 <semantics>\u0000 <mi>k</mi>\u0000 <annotation>$k$</annotation>\u0000 </semantics></math>-space data based on realistic digital VICTRE phantoms. The challenge consisted of three phases: training, validation, and testing, with a focus on evaluating reconstruction performance using relative root mean square error (rRMSE). Both traditional optimization and deep learning (DL)-based methods were allowed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The challenge attracted 42 teams from six countries, with seven progressing to the final phase. The rRMSE ranged in [0.0345, 1.24]. The top-performing algorithm employed a cascaded U-Net architecture for image denoising and parameter fitting. Overall, the competition highlighted the potential of advanced methodologies, particularly DL, in addressing complex inverse problems in medical imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The IVIM-dMRI grand challenge demonstrated significant advancements in the accuracy and robustness of dMRI reconstruction. Although the simulation-based approach provided a controlled environment, future efforts must address real-world complexities to ensure clinical applicability.</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":"144635143","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}
Suya Li, Kaushik Dutta, Debojyoti Pal, Kooresh I. Shoghi
{"title":"Noise-aware system generative model (NASGM): positron emission tomography (PET) image simulation framework with observer validation studies","authors":"Suya Li, Kaushik Dutta, Debojyoti Pal, Kooresh I. Shoghi","doi":"10.1002/mp.17962","DOIUrl":"https://doi.org/10.1002/mp.17962","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Simulation of positron emission tomography (PET) images is critical in dynamic imaging protocol optimization, quantitative imaging metric development, deep learning applications, and virtual imaging trials. These applications rely heavily on large volumes of simulated PET data. However, the current state-of-the-art PET image simulation platform is time-prohibitive and computationally intensive. Although deep learning-based generative models have been widely applied to generate PET images, they often fail to adequately account for the differing acquisition times of PET images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study seeks to develop and validate a novel deep learning-based method, the noise-aware system generative model (NASGM), to simulate PET images of different acquisition times.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>NASGM is based on the conditional generative adversarial network and features a novel dual-domain discriminator that contains a spatial and a frequency branch to leverage information from both domains. A transformer-based structure is applied for the frequency discriminator because of its ability to encode positional information and capture global dependencies. The study is conducted on a simulated dataset, with a public PET/CT dataset as the input activity and attenuation maps, and an analytical PET simulation tool to simulate PET images of different acquisition times. Ablation studies are carried out to confirm the necessity of adopting the dual-domain discriminator. A comprehensive suite of evaluations, including image fidelity assessment, noise measurement, quantitative accuracy validation, task-based assessment, texture analysis, and human observer study, is performed to confirm the realism of generated images. The Wilcoxon signed-rank test with Bonferroni correction is applied to compare the NASGM with other networks in the ablation study at an adjusted <i>p</i>-value <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≤</mo>\u0000 <mn>0.01</mn>\u0000 </mrow>\u0000 <annotation>$ le 0.01$</annotation>\u0000 </semantics></math>, and the alignment of features between the generated and target images is measured by the concordance correlation coefficient (CCC).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The quantitative accuracy measured by the correlation of mean recovery coefficients of tumor groups, and the NASGM-generated 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.17962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624751","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}
Sandro Donato, Simone Caputo, Luca Brombal, Bruno Golosio, Renata Longo, Giuliana Tromba, Raffaele G. Agostino, Gianluigi Greco, Benedicta Arhatari, Chris Hall, Anton Maksimenko, Daniel Hausermann, Darren Lockie, Jane Fox, Beena Kumar, Sarah Lewis, Patrick C. Brennan, Harry M. Quiney, Seyedamir T. Taba, Timur E. Gureyev
{"title":"Comparison of three reconstruction algorithms for low-dose phase-contrast computed tomography of the breast with synchrotron radiation","authors":"Sandro Donato, Simone Caputo, Luca Brombal, Bruno Golosio, Renata Longo, Giuliana Tromba, Raffaele G. Agostino, Gianluigi Greco, Benedicta Arhatari, Chris Hall, Anton Maksimenko, Daniel Hausermann, Darren Lockie, Jane Fox, Beena Kumar, Sarah Lewis, Patrick C. Brennan, Harry M. Quiney, Seyedamir T. Taba, Timur E. Gureyev","doi":"10.1002/mp.17950","DOIUrl":"https://doi.org/10.1002/mp.17950","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Phase-contrast breast CT imaging holds promise for improved diagnostic accuracy, but an optimal reconstruction algorithm must balance objective image quality metrics with subjective radiologist preferences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study systematically compares three reconstruction algorithms—filtered back projection (FBP), unified tomographic reconstruction (UTR), and customized simultaneous algebraic reconstruction technique (cSART)—to identify the most suitable approach for phase-contrast breast CT imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Fresh mastectomy samples were scanned at the Australian synchrotron using monochromatic 32 keV X-rays, a mean glandular dose of 2 mGy, flat-panel detectors with 0.1 mm pixels, and 6-m distance between the rotation stage and the detector. Paganin's phase retrieval method was used in conjunction with all three CT reconstruction algorithms. Objective metrics, including spatial resolution, contrast, signal-to-noise, and contrast-to-noise, were evaluated alongside subjective assessments by seven experienced radiologists. Ratings included perceptible contrast, sharpness, noise, calcification visibility, and overall quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>cSART excelled in objective metrics, outperforming UTR and FBP. However, subjective evaluations favored FBP due to its higher image contrast, revealing a discrepancy between objective and subjective assessments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The findings highlight the contrast-focused nature of radiologists’ subjective assessments and the potential of cSART for delivering superior objective image quality. These insights inform the development of hybrid evaluation tools and guide clinical translation for future live patient imaging studies.</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.17950","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624758","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}
Parmita Mondal, Allison Shields, Mohammad Mahdi Shiraz Bhurwani, Kyle A. Williams, Sricharan S. Veeturi, Swetadri Vasan Setlur Nagesh, Adnan H. Siddiqui, Ciprian N. Ionita
{"title":"Analysis of quantitative angiography in intracranial aneurysm using projection foreshortening correction and injection bias removal","authors":"Parmita Mondal, Allison Shields, Mohammad Mahdi Shiraz Bhurwani, Kyle A. Williams, Sricharan S. Veeturi, Swetadri Vasan Setlur Nagesh, Adnan H. Siddiqui, Ciprian N. Ionita","doi":"10.1002/mp.17965","DOIUrl":"https://doi.org/10.1002/mp.17965","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In neurovascular disease applications, 2D quantitative angiography (QA) based on digital subtraction angiography (DSA), is an intraoperative methodology used to assess disease severity and guide treatment. However, despite DSA's ability to produce detailed 2D projection images, the inherent dynamic 3D nature of blood flow and its temporal aspects can distort key hemodynamic parameters when reduced to 2D. This distortion is primarily due to biases such as projection-induced foreshortening and variability from manual contrast injection.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract time density curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm impulse response function (IRF) and its associated parameters peak height (PH<sub>IRF</sub>), area under the curve (AUC<sub>IRF)</sub>, and mean transit time (MTT). The effectiveness of PLC and SVD in eliminating injection bias is assessed by examining the slope of MTT versus injection duration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our findings revealed that projection and injection parameters significantly affect key quantitative angiographic parameters such as PH<sub>IRF</sub>, AUC<sub>IRF</sub>, and MTT. Our approach utilizing PLC followed by SVD-based deconvolution consistently reduced these effects from a slope of 0.363 ± 0.179–0.015 ± 0.017 across in-silico and from 0.842 ± 0.07–0.031 ± 0.015 in-vitro settings, yielding stable and reliable measurements which were correlated only with the hemodynamic conditions.</p>\u0000 </section>\u0000 \u0000 <section>\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":"144624761","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":"Feasibility of a real-time dual energy markerless monitoring of lung tumors using a clinical room-mounted stereoscopic and monoscopic x-ray imaging system","authors":"Zakary McLure, Chris Peacock, Mike Sattarivand","doi":"10.1002/mp.17966","DOIUrl":"https://doi.org/10.1002/mp.17966","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The motion of lung tumors during breathing poses challenges in stereotactic body radiotherapy (SBRT), warranting improved monitoring techniques. Breathing complicates SBRT by creating positional uncertainty in the lungs, traditionally managed with PTV margins, respiratory gating, or breath hold, each with significant drawbacks. While external and implanted markers for tracking have limitations, dual energy (DE) imaging offers a noninvasive, markerless solution that enhances soft tissue contrast and improves real-time tumor localization accuracy and precision.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to develop a markerless real-time DE tumor localization technique on a clinical room-mounted x-ray image guidance system to allow precise 3D stereoscopic and monoscopic lung tumor motion monitoring during radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A motorized programmable breathing phantom combined with an anthropomorphic phantom was developed to simulate a lung tumor's respiratory motion, with various asymmetric 3D printed tumor models from lung patients. Tumor sizes ranged between 1.0 and 3.3 cm, with some having varying densities and imaged with varying doses. Real-time images were acquired with a clinical ExacTrac stereoscopic imaging system at a rate of 1.67 Hz with high and low energies (140 and 60 kVp). Weighted logarithmic subtraction and an anti-correlated noise reduction algorithm were used to generate DE images. Conventional single energy images (120 kVp) were acquired for comparison. Digital reconstructed radiographs from x-ray imaging views were created to serve as templates for a template-matching algorithm developed to localize tumor locations on x-ray views. For the stereoscopic case where both imaging views were available, 3D triangulation was performed to localize the tumor. In the monoscopic case, when only one x-ray view was available, the 3D tumor position was estimated using a single 2D localization, combined with a 3D probability density function (PDF) describing tumor motion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Stereoscopic DE techniques demonstrated accurate localizations. The monoscopic view obstructed by the spine showed lower success rates than the view obstructed only by the rib bone. In stereoscopic cases, the localization success rates were similar (>96%) between single and DE techniques for large tumor sizes. As tumor sizes decreased, the localization success rates were higher for DE than the single energy technique showing an impro","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.17966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635189","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}
Lingshu Yin, Daniel Sforza, Devin Miles, Umezawa Masumi, Kan Ota, Xun Jia, Heng Li
{"title":"Commissioning of a 142.4 MeV ultra-high dose rate (UHDR) proton beamline in a synchrotron-based proton therapy system","authors":"Lingshu Yin, Daniel Sforza, Devin Miles, Umezawa Masumi, Kan Ota, Xun Jia, Heng Li","doi":"10.1002/mp.18008","DOIUrl":"https://doi.org/10.1002/mp.18008","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Recent studies suggest that radiotherapy at ultrahigh dose rates (>40 Gy/s, FLASH) offers normal tissue sparing effects while maintaining tumor control. There is significant interest in preclinical studies investigating the mechanism of FLASH sparing effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to commission a fixed proton beamline within a synchrotron-based proton therapy system for preclinical proton FLASH research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Modifications were made to the Hitachi PROBEAT-CR synchrotron system to enhance RF extraction power and increase proton beam current at 142.4 MeV. A high-speed electrometer and an optimized transmission ion chamber (IC) were implemented for ultra-high dose rate (UHDR) beam monitoring and delivery, replacing the conventional beam monitoring IC. Beam output was measured using a Faraday cup in both UHDR and clinical modes. Gafchromic film measurements and Monte Carlo simulations were employed to validate dose delivery in a solid water phantom with various spot scanning patterns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The calibration of transmission IC against Faraday cup shows sufficient charge collection efficiency at both clinical dose rates and UHDR. The UHDR PBS beamline demonstrates better than 1% reproducibility and linearity in the absolute beam output. Due to the limited charge per spill, the delivered dose per spill is inversely proportional to the field size. However, the system can deliver up to 41.4 Gy (268.1 Gy/sec) at 2 cm depth with a field size (FWHM) of 8.2 mm, demonstrating suitability for small animal proton FLASH irradiation studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We successfully commissioned a fixed beam proton UHDR PBS beamline in a synchrotron-based proton therapy system. Despite synchrotron-specific system constraints, our system enables controlled UHDR delivery for preclinical proton FLASH research.</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.18008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635207","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}