Medical physics最新文献

{"title":"A deep-learning-based pipeline for automatic fusion of CT coronary angiogram and stress perfusion CMR","authors":"Wenting Jiang,&nbsp;Ming-Yen Ng,&nbsp;Tsun-Hei Sin,&nbsp;Peng Cao","doi":"10.1002/mp.70420","DOIUrl":"10.1002/mp.70420","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Accurate evaluation of coronary artery constriction and myocardial ischemia is essential for diagnosing and managing coronary artery disease (CAD). Combining CT coronary angiography (CTCA) and stress cardiovascular magnetic resonance (CMR) imaging allows examination of both coronary artery narrowing and myocardial perfusion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To develop a deep learning pipeline that integrates CTCA and CMR images, which could help improve accuracy in identifying affected vessels and their associated myocardial territories.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed pipeline included two deep learning models: one for automatic reorientation of 3D CTCA and another for left ventricle (LV) wall registration between CTCA and CMR images. A 3D spatial co-registration model, the reorientation spatial transformer network (Reorientation STN), predicted reorientation parameters for input CTCA volumes using ResNet18 and STN. A 2D nonrigid spatial deformation network (Nonrigid SDN) was trained for LV wall registration. Cross-modal supervision was employed during training. Evaluation criteria included aspect ratio (AR), Dice similarity coefficient (DSC), and long-axis deviation angles. The process involved quantifying LV wall perfusion on registered CMR images and extracting coronary arteries from reoriented CTCA images to fuse these results. The pipeline was trained and validated on 447 pairs of CTCA and CMR images from 75 patients and tested on 18 subjects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The pipeline achieved an AR of 0.94 ± 0.03, long-axis deviation angles of 1.19 ± 0.83 (axial) and 1.54 ± 0.79 (coronal), a DSC of 0.66 ± 0.04 for LV wall reorientation, and a DSC of 0.92 ± 0.03 for LV wall registration between CTCA and CMR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This automated framework successfully fuses cardiac CTCA and CMR imaging, demonstrating its potential effectiveness.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147648063","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":"Accurate iodine quantification and residual error reduction with principal component analysis multimaterial decomposition using spectral CT","authors":"Hamidreza Khodajou-Chokami,&nbsp;Huanjun Ding,&nbsp;Sabee Molloi","doi":"10.1002/mp.70407","DOIUrl":"10.1002/mp.70407","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;Multimaterial decomposition (MMD) in dual-energy CT enables iodine quantification, critical for diagnostic applications. However, residual errors from noniodine materials in iodine maps limit accuracy, especially in complex thoracic regions and low-dose settings.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate iodine quantification accuracy and residual error using a principal component analysis multimaterial decomposition (PCA-MMD) algorithm on dual-energy CT data across different phantom sizes and radiation dose levels.&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 thorax phantom containing iodine (2–20 mg/mL) and calcium (50–400 mg/mL) inserts was scanned on a clinical photon-counting CT system. Three phantom sizes (small: 20.9 cm, medium: 27.3 cm, large: 33.2-cm water-equivalent diameter) were imaged at dose levels ranging from 3 to 55 mGy. The PCA-MMD algorithm applies a principal component analysis (PCA) transformation followed by direct geometric estimation with barycentric coordinates, thereby avoiding matrix inversion instability. Iodine quantification was evaluated using linear regression, root mean square error (RMSE), and coefficient of variation (CV). Residual error in noniodine regions was expressed as a percentage of the minimum detectable iodine concentration. The algorithm's performance was also evaluated through a clinical proof-of-concept study involving five patients, comparing virtual noncontrast (VNC) images to true noncontrast (TNC) references.&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;PCA-MMD achieved near-unity regression slopes (0.98–0.99, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;≥&lt;/mo&gt;\u0000 &lt;mn&gt;0.996&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$R^2 ge 0.996$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) across all phantom sizes, reducing RMSE by up to 65% compared to the standard barycentric coordinate-based MMD (0.10–0.39 vs. 0.20–0.72 mg/mL). Residual error was markedly lower with PCA-MMD (0.7–1.6%) than with the standard barycentric coordinate-based MMD (16.1%–54.9%) under identical conditions. At 3 mGy, PCA-MMD achieved an RMSE of 0.60 mg/mL versus 0.95 mg/mL for the standard barycentric coordinate-based MMD. In the clinical cohort, PCA-MMD significantly improved VNC accuracy, achiev","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147648022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of extrapolation chamber response for surface and buildup dose assessment in radiotherapy photon beams using Monte Carlo simulations","authors":"Cristiano Q. M. Reis,&nbsp;Bryan R. Muir,&nbsp;Patricia Nicolucci,&nbsp;D. W. O. Rogers","doi":"10.1002/mp.70408","DOIUrl":"10.1002/mp.70408","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;Surface dose assessment is essential in radiotherapy, but accurately measuring doses in the buildup region of megavoltage beams presents significant challenges. Extrapolation chambers are frequently regarded as the most suitable detectors for this purpose.&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 establish under what conditions extrapolation chambers can be used to measure surface doses and to use Monte Carlo calculations to prove that measured surface-to-maximum ionization ratios correspond to absorbed-dose ratios. A secondary purpose is to understand why surface measurements with a Markus fixed parallel-plate chamber are inaccurate.&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;EGSnrc applications were used for calculating the dose to the air cavity of two extrapolation chambers in a polystyrene phantom as a function of the electrode separation (also called gap), &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;s&lt;/mi&gt;\u0000 &lt;annotation&gt;$s$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, between 0.1 and 10 mm. Doses to the phantom, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mi&gt;pst&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm D}_{rm pst}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, at depths corresponding to the effective point of measurement (EPOM) of the chambers were also calculated. Calculations were performed using clinical photon beams (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mn&gt;60&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mi&gt;Co&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$^{60}{rm Co}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, 6, 10, and 25 MV) that were fully modeled using BEAMnrc. Calculated chambers' responses as a function of the gap were compared with experimental data from the literature. Variations of the replacement correction factor (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;repl&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$P_{rm repl}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and the wall perturbation factor (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13055151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629661","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":"XACT-guided interventional procedures: A feasibility study","authors":"Yuchen Yan,&nbsp;Prabodh Kumar Pandey,&nbsp;Yifei Xu,&nbsp;Leshan Sun,&nbsp;Nadine Abi-Jaoudeh,&nbsp;Zhehui Wang,&nbsp;Shawn (Liangzhong) Xiang","doi":"10.1002/mp.70401","DOIUrl":"10.1002/mp.70401","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>X-ray Induced Acoustic Computed Tomography (XACT) is an emerging imaging technique that provides 3D volumetric images from a single projection by detecting X-ray-induced acoustic (XA) waves with ultrasound (US) transducers. Unlike MRI or CT, XACT enables real-time 3D imaging without long acquisition times, 2D limitations, or motion artifacts from mechanical rotation. Furthermore, when integrated with conventional pulse-echo US through time-sequenced operation, XACT provides complementary soft-tissue contrast, establishing it as a promising platform for dynamic interventional radiology (IR) guidance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to evaluate the feasibility of XACT as a novel imaging tool for IR, specifically for guiding needle placement and monitoring contrast agent dynamics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a 3D XACT imaging system equipped with a 256-element 2D matrix array and a portable X-ray tube to visualize needle insertion and contrast agent dynamics. To capture both needle position and soft tissue anatomy, we integrated a dual-modality system combining XACT with pulse-echo US using a 128-element linear US array. Experiments were conducted on both tissue-mimic phantoms and soft tissue samples. GPU-accelerated algorithm has been developed for 3D XACT image reconstruction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results demonstrate the 3D capabilities of XACT imaging for interventional procedures, including monitoring of needle placement and tracking of contrast agent dynamics. The imaging speed reached up to 3∼4 s per frame, constrained by the repetition rate of the X-ray source and signal to noise ratio. The dual-modality approach provided clear visualization of the needle's position and the surrounding soft tissue structures, achieving imaging resolution around 2 mm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study demonstrates that XACT imaging is feasible to be used for IR. Its 3D imaging capability with faster imaging speed would be an alternative to cone beam CT and its capability to combine US imaging will provide richer information for soft tissue</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625020","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":"Evaluation of a water phantom detector-applicator setup for independent experimental verification of 106Ru ocular brachytherapy applicators","authors":"Simon Dahlander,&nbsp;Michael Andrássy,&nbsp;Daniela Frömberg,&nbsp;Linda Persson,&nbsp;Åsa Carlsson Tedgren","doi":"10.1002/mp.70404","DOIUrl":"10.1002/mp.70404","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;&lt;b&gt;&lt;sup&gt;106&lt;/sup&gt;&lt;/b&gt;Ru ophthalmic brachytherapy (BT) applicators are used for treating ocular tumors. While manufacturer-provided dosimetry data is commonly used for treatment planning, independent quality assurance (QA) measurements are crucial. However, there is a lack of dedicated equipment and standardized protocols for clinical verification of &lt;sup&gt;106&lt;/sup&gt;Ru depth-dose distributions.&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;The BetaCheck-106™ is a prototype compact water phantom detector-applicator setup enabling high precision alignment of &lt;sup&gt;106&lt;/sup&gt;Ru applicators and detectors. We assess the setup's compatibility with three commercially available detectors (microSilicon, microSilicon X and microDiamond) and its performance in determining full 1D depth-current curves with these detectors. In addition, data from clinical QA-tests of 20 &lt;sup&gt;106&lt;/sup&gt;Ru applicators was used to estimate inhomogeneities in the applicator's &lt;sup&gt;106&lt;/sup&gt;Ru coating.&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;Measurements were conducted on two &lt;sup&gt;106&lt;/sup&gt;Ru applicators with different activity levels, one had been in clinical service for one year and one was measured before clinical use. Dose rates were recorded at 1 mm intervals from 2 mm to 10 mm in water using the BetaCheck-106™ setup with the three detectors. Measurement precision and detector response were analyzed. Separately, inter-applicator variability was analyzed using the aforementioned measurements of 20 &lt;sup&gt;106&lt;/sup&gt;Ru applicators.&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 BetaCheck-106™ demonstrated exceptional setup reproducibility (0.23%), enabling precise depth-resolved measurements. Both of the silicon diode detectors examined provided stable and reproducible measurements. The diamond detector performed reproducibly for the high-activity applicator but exhibited depth-dependent signal instability for the low-activity source, likely due to the detector's lower sensitivity. Normalized depth-signal curves for the three detectors all had similar shape. Analysis of measured current per activity of 20 &lt;sup&gt;106&lt;/sup&gt;Ru applicators revealed a depth dependent inhomogeneity effect decreasing with depth.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The BetaCheck-106™ provides practical high-reproducibility positioning of detector and applicator for &lt;sup&gt;106&lt;/sup&gt;Ru applicator measurements in water. The silicon detectors successfully char","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625052","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":"Direct estimation of activity concentration in regional voxels with application to 177Lu peptide receptor radionuclide therapy","authors":"Johan Gustafsson,&nbsp;Erik Larsson,&nbsp;Katarina Sjögreen Gleisner","doi":"10.1002/mp.70424","DOIUrl":"10.1002/mp.70424","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;Quantitative SPECT in radionuclide-therapy is limited by partial-volume effects (PVEs). The implementation of regional voxels (r.v.), estimating mean activity concentrations in regions directly from projections, offers a promising alternative for the geometry-specification to reduce PVEs.&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 aims to demonstrate that activity-concentration estimation with r.v. is superior to reconstruction with cuboid voxels (cu.v.) with post-reconstruction partial-volume correction (PVC) for estimation of activity concentration in &lt;sup&gt;177&lt;/sup&gt;Lu peptide receptor radionuclide therapy (&lt;sup&gt;177&lt;/sup&gt;Lu-PRRT).&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;Data originated from one patient administered [&lt;sup&gt;177&lt;/sup&gt;Lu]Lu-DOTA-TOC with SPECT acquired at 1 d, 4 d and 7 d p.i. stored in list-mode format (dataset PA), and eight patients given [&lt;sup&gt;177&lt;/sup&gt;Lu]Lu-DOTA-TATE with SPECT acquired 1 d p.i. (dataset PB). Activity concentration was estimated from reconstruction with cu.v. and using r.v. for both datasets, with multiple noise realizations for PA using bootstrapping. Organ delineation was performed based on CT using the AI tool TotalSegmentator, and tumor delineation made in cu.v. SPECT images. The estimated activity concentration for kidneys, spleen, and tumors from r.v. was compared to that obtained with cu.v. with and without post-reconstruction PVC. To study the accuracy of activity-concentration estimates, simulations were performed with the SIMIND Monte Carlo program with patient images used as basis. The sensitivity to misalignments between SPECT and CT was also evaluated.&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;For both patient and simulated data, activity concentrations estimated with r.v. are higher than those from cu.v., with comparable standard deviations. Mean relative errors for simulated images from PA relative to simulation input at 1 d p.i. reconstructions with r.v. are (−4.6 &lt;i&gt;± &lt;/i&gt;1.4) %, (3.0 &lt;i&gt;± &lt;/i&gt;0.5) %, (0.1 &lt;i&gt;± &lt;/i&gt;0.5) %, and (5.6 &lt;i&gt;± &lt;/i&gt;1.2) % for tumor, left kidney, right kidney, and spleen, respectively. Corresponding results for cu.v. with post-reconstruction PVC are (−12.3 &lt;i&gt;± &lt;/i&gt;2.2) %, (-4.2 &lt;i&gt;± &lt;/i&gt;0.6) %, (−7.0 &lt;i&gt;± &lt;/i&gt;0.5) %, and (-2.1 &lt;i&gt;± &lt;/i&gt;1.1) %. For simulated images based on PB, the mean relative errors obtained for r.v. are (−3.1 &lt;i&gt;± &lt;/i&gt;3.5) %, (1.2 &lt;i&gt;± &lt;/i&gt;1.2) %, (−1.7 &lt;i&gt;± &lt;/i&gt;1.1) %, and (2.3 &lt;i&gt;± &lt;/i&gt;0.8) %, while for cu.v. with PVC they are (−7.9 &lt;i&gt;± &lt;/i&gt;6.7) %, (-5.8 &lt;i&gt;± &lt;/i&gt;1.9) %, (−9.0 &lt;i&gt;± &lt;/i&gt;","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625006","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":"Beam-hardening correction in clinical x-ray dark-field chest radiography using deep-learning-based bone segmentation","authors":"Lennard Kaster,&nbsp;Maximilian E. Lochschmidt,&nbsp;Anne M. Bauer,&nbsp;Tina Dorosti,&nbsp;Sofia Demianova,&nbsp;Thomas Koehler,&nbsp;Daniela Pfeiffer,&nbsp;Franz Pfeiffer","doi":"10.1002/mp.70422","DOIUrl":"10.1002/mp.70422","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;Dark-field radiography is a novel x-ray imaging modality that provides complementary diagnostic information by visualising microstructural properties of lung tissue. Implemented via a Talbot–Lau interferometer integrated into a conventional x-ray system, it permits simultaneous acquisition of perfectly registered attenuation and dark-field radiographs. Clinical studies have shown that dark-field radiography outperforms conventional radiography in diagnosing and staging pulmonary diseases, yet the polychromatic nature of medical x-ray sources causes beam hardening and introduces structured artifacts, especially from ribs and clavicles.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To address the artificial dark-field signal arising from beam-hardening and thereby improve the reliability of clinical dark-field chest radiography by suppressing bone-induced artifacts.&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 segmentation-based beam-hardening correction (BHC) was developed that employs deep learning to segment ribs and clavicles and uses attenuation-contribution masks derived from dual-layer detector computed-tomography data to refine the material distribution and estimate beam-hardening effects. The rib segmentation network was trained on 196 chest radiographs with 49 validation images (VinDr-RibCXR), and a clavicle network was trained on 56 images with 12 validation and 12 test cases. The trained models were applied to 174 dark-field chest radiographs (51 chronic obstructive pulmonary disease, 86 COVID-19, 37 healthy) and spectral CT scans from two patients; input data consisted of attenuation and dark-field images and outputs were corrected dark-field images and derived lung-signal metrics.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The proposed method markedly reduced bone-induced artifacts and improved the homogeneity of the lung dark-field signal. In comparative analyses, the corrected images exhibited diminished structured cross-talk between attenuation and dark-field channels, enhancing both visual interpretation and quantitative consistency across cohorts.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;By combining deep-learning-based anatomical segmentation with material-specific attenuation weighting, the proposed BHC suppresses the artificial dark-field signal caused by polychromatic x-ray spectra, leading to more reliable assessment of pulmonary microstructure in clinical dark-field chest","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13049107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147616729","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":"An efficient method for evaluating the lead equivalence of x-ray radiation protective equipment using an analytical spectrum model","authors":"Sewa Surdashi,&nbsp;Aseel Aziz,&nbsp;Shahla Mobini Kesheh,&nbsp;Jörgen Scherp Nilsson,&nbsp;Artur Omar","doi":"10.1002/mp.70421","DOIUrl":"10.1002/mp.70421","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;X-ray radiation protective equipment is essential for ensuring the safety of medical staff. It is therefore important to verify its effectiveness, including confirming the specified lead equivalence (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Pb&lt;/mi&gt;\u0000 &lt;mi&gt;eq&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm Pb}_{rm eq}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), as it is a recognized standard protective value. Current methods require multiple comparative measurements with reference lead sheets, rendering the process laborious, susceptible to errors, and challenging to apply across a large medical facility with diverse protective equipment.&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 introduce an efficient method for evaluating lead equivalence based on a computational model involving analytical spectrum modeling.&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 method consists of measuring the transmission of the protective equipment and then translating it into a lead-equivalent thickness using a computational model. In this work, an example implementation is presented utilizing the SpekPy toolkit for spectrum modeling. To validate the method, it was used to estimate the thickness of high-purity lead sheets with known thicknesses (0.1–1.0-mm Pb). Furthermore, its application is demonstrated for two lead-free aprons (0.25- and 0.35-mm &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Pb&lt;/mi&gt;\u0000 &lt;mi&gt;eq&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm Pb}_{rm eq}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), a lead-vinyl apron (0.5-mm &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Pb&lt;/mi&gt;\u0000 &lt;mi&gt;eq&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm Pb}_{rm eq}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), a lead–acrylic and a lead–plywood mobile screen (0.5- and 1.0-mm &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Pb&lt;/mi&gt;\u0000 &lt;mi&gt;eq&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm Pb}_{rm eq}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). Because the approach is based on measuring the transmission utilizing the primary x-ray tube beam (rather than scatter from a phantom), Monte Carlo (M","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13049111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615806","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":"Performance benchmarking of deep learning models for real-time median nerve segmentation and cross-sectional area measurement in ultrasound imaging","authors":"Vaddadi Venkatesh,&nbsp;Lokesh Bathala,&nbsp;Raji Susan Mathew,&nbsp;Phaneendra K. Yalavarthy","doi":"10.1002/mp.70414","DOIUrl":"10.1002/mp.70414","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;Median nerve, a major peripheral nerve, connects the hand to the central nervous system, facilitating upper limb motor function and sensation by transmitting sensory data from the palm and fingers. Damage to this nerve can result in motor and sensory deficits, with carpal tunnel syndrome (CTS) causing compression, leading to tingling and numbness in the thumb, index, middle, and lateral ring fingers.&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 develop an accurate deep-learning-based segmentation method for measuring the cross-sectional area (CSA) of the median nerve to facilitate the diagnosis of nerve entrapment syndromes and aid in surgical planning, with a focus on CTS.&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;This study introduces MNSeg-Net, a novel lightweight multiscale feature fusion network with 2.46M parameters for median nerve segmentation in ultrasound (US) frames, specifically designed to enable a fully automated, end-to-end clinical setup supporting real-time segmentation and CSA computation. The dataset comprised 100 subjects and 30 000 ultrasound frames, which were split into training (80%), validation (10%), and testing (10%) subsets with subject-wise separation to avoid data leakage. MNSeg-Net was benchmarked against state-of-the-art segmentation models, including UNet and its variants (UNet++ and U2Net). The performance was assessed using metrics such as the Dice similarity coefficient (DSC) and CSA difference. The statistical significance of performance differences was evaluated using paired &lt;i&gt;t&lt;/i&gt;-tests, effect size (Cohen's &lt;i&gt;d&lt;/i&gt;), and one-way ANOVA with Tukey's HSD correction for multiple comparisons at a &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;annotation&gt;$p$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-value threshold of 0.05, while statistical equivalence between models within predefined margins was formally assessed using the two one-sided test (TOST) procedure. Following quantitative validation, the model was deployed in a real-time clinical setup utilizing an Av.io HD Epiphan frame grabber to stream ultrasound images from the ultrasound machine to a GPU-equipped system. A secondary display running parallel to the original ultrasound screen visualized the segmented median nerve and computed the CSA values in real time.&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;MNSeg-Net achieved high segmentation performance, with average DSC scores of 94.7% at the wrist and 83.4% from the wrist to the elbow, and the lowest Hausdorff distance, matching the performance of the bes","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147616939","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":"Impact of partial volume correction on radiomics reproducibility in theranostic SPECT/CT imaging","authors":"Mohammad Saber Azimi,&nbsp;Maryam Cheraghi,&nbsp;Mohammad Ali Ghodsi Rad,&nbsp;Mohadeseh Bayat,&nbsp;Maryam Alhashim,&nbsp;Habibollah Dadgar,&nbsp;Mahmoud Alabedi,&nbsp;Nahid Ibrahim,&nbsp;Enas Alwuhaib,&nbsp;Hossein Arabi,&nbsp;Arman Rahmim,&nbsp;Habib Zaidi","doi":"10.1002/mp.70427","DOIUrl":"10.1002/mp.70427","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Background&lt;/b&gt;: Radiomics has shown potential for quantitative characterization of tumors in molecular imaging; however, its clinical translation in theranostic &lt;sup&gt;1&lt;/sup&gt;&lt;sup&gt;7&lt;/sup&gt;&lt;sup&gt;7&lt;/sup&gt;Lu SPECT/CT remains limited due to poor robustness of extracted features to reconstruction variability and partial volume effects. Establishing reproducible radiomics biomarkers across correction strategies is therefore a prerequisite for reliable clinical modeling and treatment monitoring.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Purpose&lt;/b&gt;: This study aimed to evaluate radiomics feature reproducibility, defined as the stability of feature values across different partial volume correction (PVC) strategies and reconstruction settings, in clinical &lt;sup&gt;1&lt;/sup&gt;&lt;sup&gt;7&lt;/sup&gt;&lt;sup&gt;7&lt;/sup&gt;Lu SPECT/CT imaging. In addition, we explored two volumetric shape-based indices, the metastasis-to-liver ratio (MLR) and metastasis-to-spare liver ratio (MSLR), as surrogate markers of hepatic metastatic burden in the theranostic treatment setting.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Methods&lt;/b&gt;: In 13 patients (40 scans) treated with &lt;sup&gt;177&lt;/sup&gt;Lu, 837 radiomics features were extracted from 11 abdominal regions and metastases on SPECT/CT using original and wavelet-decomposed images across four bin widths (50–200). Two post-reconstruction PVC methods, namely Richardson-Lucy (RL) and Reblurred Van Cittert (RVC), were applied. Feature reproducibility was quantified using two complementary metrics: the intraclass correlation coefficient (ICC) to assess feature-level stability across PVC strategies, and the concordance correlation coefficient (CCC) to evaluate pairwise agreement and systematic bias among reconstruction methods. Visual image quality assessments were independently performed by two experienced nuclear medicine specialists in a blinded setting. Exploratory metastatic tumor burden was assessed descriptively using 3D shape-based MLR and MSLR indices.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Results&lt;/b&gt;: Low-frequency wavelet decomposition (LLL-wavelet) and original features showed the highest reproducibility (ICC ≥ 0.90 in &gt;95% of liver and metastasis features at BW50), whereas high-frequency features and larger bin widths demonstrated reduced stability. CCC analysis revealed excellent agreement between RL and RVC (≥0.95 in major organs at BW50–100), while agreement with uncorrected SPECT (no PVC) was consistently lower, especially for high-frequency features. RL achieved higher visual scores in sharpness and contrast (&lt;i&gt;p&lt;/i&gt; &lt; 0.01), with good inter-reader agreement supporting the consistency of these assessments. MLR/MSLR demonstrated inter-patient variability and were explored descriptively as indices of metastatic liver burden.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Conclusions&lt;/b&gt;: Reproducibility in theranostic SPECT radiomics is highly feature- and organ-dependent and is further influenced by scanner-specific factors and reconstruction protocols, which remain critical for real-world clinical translation. RL and RVC showed stronger mutual agreements than each wit","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147616941","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}