Medical physics最新文献

{"title":"Simulation study of wedge-shaped light guide for compensating depth-of-interaction induced timing uncertainty in Cherenkov radiator.","authors":"Hyungu Kang, Hyeong Seok Shim, Minseok Yi, Juhwan Kim, Yoonchan Jeong, Jae Sung Lee","doi":"10.1002/mp.70655","DOIUrl":"10.1002/mp.70655","url":null,"abstract":"<p><strong>Background: </strong>Coincidence timing resolution (CTR) is a key performance factor in PET systems. Cherenkov radiation, due to its ultrafast emission on the femtosecond scale, offers potential for achieving improved timing resolution compared to traditional scintillation-based detectors. However, the inherently low photon yield in pure Cherenkov radiators makes depth-of-interaction (DOI) estimation and its associated timing uncertainty a significant challenge, especially when trying to achieve sub-30 ps CTR.</p><p><strong>Purpose: </strong>This study aims to reduce DOI-induced timing uncertainty in Cherenkov-based PET detectors by proposing a novel wedge-shaped light guide design. The goal is to improve the CTR performance by compensating for optical path differences resulting from varying interaction depths.</p><p><strong>Methods: </strong>A Monte Carlo simulation framework was developed to model gamma-ray interactions within a Cherenkov radiator, incorporating electron scattering and Cherenkov photon generation. Two detector geometries were compared: one with a conventional planar light guide and another with the proposed wedge-shaped light guide. The wedge design was tailored to delay early-arriving photons and synchronize photon transit times, thereby reducing time spread. The simulation tracked photon paths through the wedge and evaluated performance using metrics such as spatial resolution, timing spread, and sensitivity.</p><p><strong>Results: </strong>Simulations demonstrated that the wedge-shaped light guide significantly reduced DOI-induced timing uncertainty-by 26.3% in a 3 mm-thick Cherenkov radiator-compared to the conventional configuration. The wedge geometry also enabled improved spatial resolution in multi-layer detector configurations. However, a trade-off with reduced sensitivity was observed. To address this, strategies such as utilizing discarded photons via refractive index manipulation and cascade detector structures were proposed.</p><p><strong>Conclusions: </strong>The wedge-shaped light guide effectively compensates for DOI-related path length differences and enhances CTR in Cherenkov-based PET detectors. This approach opens avenues for more accurate event localization without increasing photon yield, and could serve as a building block for advanced PET systems requiring ultrafast timing performance.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70655"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-angle fluoroscopic lung perfusion imaging using x-ray pulsatility index: A pilot study in healthy subjects.","authors":"Matthew R Smith, Bradley W Richmond, Savannah Gregory, Maggie Cartwright, Andreas Fouras, Greg T Mogel, Charles R Hatt, Gary T Smith, Jared V Grice","doi":"10.1002/mp.70666","DOIUrl":"10.1002/mp.70666","url":null,"abstract":"<p><strong>Background: </strong>X-ray pulsatility index (XPI) quantifies cardiac-synchronous attenuation changes as a surrogate for regional lung perfusion, but current implementations are limited by projectional overlap and the lack of reference values in healthy individuals.</p><p><strong>Purpose: </strong>To characterize regional XPI patterns and establish preliminary reference values in healthy participants using a novel multi-angle fluoroscopic imaging system.</p><p><strong>Methods: </strong>Nineteen healthy participants underwent an 8 s breath-hold during fluoroscopic imaging using 15 fps at four simultaneous projections (LPO, AP caudal, RPO, AP cranial) using a novel scanner. Frames were cropped to exclude initial x-ray tube stabilization and bulk motion. Spectral analysis exploited the periodic signal attenuation in the lungs to create XPI maps, as previously described. To focus on the clinically important regions of lung perfusion, the peripheral 3 cm of lung was manually segmented and divided into upper, middle, and lower lung zones. XPI values from these regions were compared to evaluate laterality and cranial-caudal differences using paired t-tests corrected with the Benjamini-Hochberg procedure to control the False Discovery Rate (FDR). Peripheral XPI contrast-to-noise (pCNR) was calculated in the AP caudal projection to quantify signal-to-background separation. Data were retrospectively resampled to assess the effect of reduced frame rate (7.5, 5 fps) and shorter acquisition time using multiple regression analysis. Radiation dose was estimated using phantom measurements and simulation.</p><p><strong>Results: </strong>All participants performed the breath-hold without difficulty. XPI maps demonstrated bilateral lung perfusion across all four views, enabling multi-projection assessment of regional perfusion. No focal defects were observed; however, one participant demonstrated globally reduced XPI. Significant cranial-caudal gradients in XPI were observed across projection angles, consistent with known gravity-dependent physiology. Following FDR adjustment (Q = 0.05), the lower zones continued to demonstrate significantly higher values than the upper zones in 3 of 4 projections (all q < 0.05). There were no significant laterality differences (q > 0.05). XPI values remained stable across acquisition lengths and frame rates, indicating robustness of the metric. CNR increased with longer acquisitions and higher frame rates, reflecting reduced noise and improved signal reliability.</p><p><strong>Conclusion: </strong>Multi-angle XPI fluoroscopy enables non-invasive regional lung perfusion assessment with low radiation exposure and provides preliminary reference values for future clinical investigation.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70666"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of motion quantification in dynamic CT images of the wrist joint across multiple CT vendors: A phantom study.","authors":"Hanne Vries, Brigitte van der Heijden, Stefan Hummelink, Iwan Dobbe, Ioannis Sechopoulos, Geert Streekstra","doi":"10.1002/mp.70667","DOIUrl":"10.1002/mp.70667","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Four-dimensional computed tomography (4D CT) allows dynamic assessment of wrist kinematics and offers a non-invasive alternative for evaluating ligament instability. However, insufficient temporal resolution and differences in acquisition or reconstruction protocols may introduce motion-related artifacts that affect quantitative analysis. As these artifacts can mimic pathological carpal motion, measurement error must be quantified to distinguish true pathology from methodological error. Therefore, systematic evaluation of motion quantification across different CT systems and acquisition protocols is required.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To evaluate the errors in motion quantification across acquisition and reconstruction protocols of different CT systems in 4D CT imaging of wrist bones.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A rotating wrist phantom with three 3D-printed bones (scaphoid, lunate, capitate) was scanned on five CT systems from three manufacturers (Aquilion ONE PRISM and ONE Vision, Canon Medical; Revolution Apex, GE HealthCare; single-source and dual-source SOMATOM Force, Siemens Healthcare). One static 3D scan and multiple 4D scans were acquired at different phantom rotation speeds, each lasting 10 s. Single-source covered 0.050-0.300 phantom rotations per second (rps), and dual-source 0.100-0.600 rps. Dose dependence was evaluated on two systems (80 kV/40 mA and 120 kV/100 mA). Images were reconstructed in full and partial modes, segmented, and registered using point-to-image registration. Motion quantification error was calculated for translation and rotation of the scaphoid and capitate relative to the lunate, referenced to the static scan, and reported per rotation cycle. Motion quantification error was additionally expressed as a function of normalized motion per reconstructed frame to relate the error to the amount of motion occurring during image acquisition. Effects of CT system, bone type, reconstruction method, and rotation speed were analyzed using a linear mixed model. Statistical significance was defined as p &lt; 0.05.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The motion quantification error was hardly affected by dose. For 0.100 rps, the errors of the best single-source system were median 0.29 mm (interquartile range: 0.21-0.36 mm) and 1.37° (0.82°-2.18°) for full and 0.21 mm (0.16-0.25 mm) and 0.70° (0.52°-0.95°) for partial reconstructions for the capitate; the dual-source system showed the lowest errors (0.14 mm (0.12-0.17 mm) and 0.48° (0.39°-0.62°)). Motion quantification error scaled approximately linearly with normalized motion per reconstructed frame, corresponding to approximately 15% of the motion occurring during one reconstructed frame.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Motion quantification errors were largely determined by phantom rotation speed and temporal resolution. The reported values provide a basis for defining the detection threshold for quantitative wrist kinematics, supportin","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70667"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13542767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable graph-conditioned CNNs for dose-volume histogram prediction in radiotherapy.","authors":"Xiaoling Zhu, Chifang Ling, Hao Xu, Kuo Men, Jianrong Dai, Weigang Hu, Zhiqiang Liu, Jiawei Fan","doi":"10.1002/mp.70663","DOIUrl":"10.1002/mp.70663","url":null,"abstract":"<p><strong>Background: </strong>Accurate dose-volume histogram (DVH) prediction is essential for high-quality and automated radiotherapy (RT) planning. However, existing deep learning methods primarily focus on voxel-level dose prediction followed by post-processing to extract DVHs, a workflow that can introduce cumulative errors and limit clinical interpretability.</p><p><strong>Purpose: </strong>In this study, we propose a novel deep learning framework that directly predicts full DVHs for multiple organs-at-risk (OARs) from computed tomography (CT) and structure contours.</p><p><strong>Methods: </strong>Our method combines a three-dimensional convolutional neural network (CNN) for anatomical feature extraction with a graph neural network (GNN) that models each DVH as a structured graph, enabling sequential dose-volume dependencies to be learned explicitly. The model was extensively validated on nasopharyngeal cancer cases from an external institute and rectal cancer cases from our affiliated hospital, encompassing treatments with TomoTherapy and volumetric modulated arc therapy (VMAT). We additionally conducted a model-guided clinical DVH assessment on suboptimal cases to demonstrate the method's clinical utility RESULTS: The proposed method achieved low dose-wise prediction errors and outperformed previous approach. Specifically, it reduced the mean dose error for the brain stem from 1.24 Gy to 0.66 Gy and for the larynx from 1.53 Gy to 0.78 Gy, with notable improvements also observed in the parotid glands and temporal lobes. Statistical analysis of clinical indices demonstrated non-inferiority to ground-truth plans (p > 0.05). Moreover, the predicted DVHs effectively guided plan revision and improved plan quality.</p><p><strong>Conclusions: </strong>Our results suggest that direct DVH prediction using a CNN-GNN framework offers a robust and clinically interpretable solution for treatment planning and quality assurance.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70663"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13542759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Model-Specific Delivery Sequence Optimization for Spot-scanning Proton Arc Therapy Using a Compact Superconducting Synchrocyclotron.","authors":"Peilin Liu, Lewei Zhao, Xiaoda Cong, Gang Liu, Xiaoqiang Li, Xuanfeng Ding","doi":"10.1002/mp.70658","DOIUrl":"10.1002/mp.70658","url":null,"abstract":"<p><strong>Background: </strong>Spot scanning proton arc therapy (SPArc) combines the dosimetric advantages of proton therapy with the beam-angle freedom of arc delivery. However, current planning algorithms rely on static delivery assumptions that do not account for the temporal characteristics of pulsed-beam synchrocyclotron systems during continuous gantry rotation. This mismatch between nominal plans and actual treatment delivery may lead to clinically meaningful dose deviations.</p><p><strong>Purpose: </strong>To develop and evaluate a dynamic arc delivery sequencing optimization framework that incorporates machine-specific delivery characteristics to minimize deviations between planned and delivered dose in SPArc.</p><p><strong>Methods: </strong>A five-step dynamic arc delivery sequencing optimization framework was developed. The framework includes: (1) static and dynamic delivery time calculation, (2) spot and energy-layer disassembling, (3) incorporation of dynamic delivery timing into control points, (4) spot-weight fine-tuning, and (5) reconstruction of energy-layer sequences. Five multi-metastatic brain stereotactic radiosurgery cases were retrospectively evaluated. Delivery accuracy, efficiency and plan quality were assessed using virtual machine logfiles.</p><p><strong>Results: </strong>The sequencing optimization framework substantially improved delivery accuracy while preserving plan quality and efficiency. For the total gross tumor volume, the mean absolute D98 deviation between planned and virtual logfile reconstructed doses decreased from 77.4 ± 81.0 cGyE (4.2 ± 4.5%) with static SPArc plans to 9.6 ± 4.0 cGyE (0.5 ± 0.2%) after sequencing optimization. For the worst metastasis in each case, D98 deviation decreased from 184.4 ± 145.2 cGyE (9.8 ± 7.9%) to 19.0 ± 16.4 cGyE (1.0 ± 0.8%), and D2 deviation decreased from 148.4 ± 114.4 cGyE (6.8 ± 5.6%) to 13.2 ± 8.6 cGyE (0.6 ± 0.4%). Target coverage and normal brain sparing remained statistically unchanged (p > 0.05), and total delivery times differed by < 1 s.</p><p><strong>Conclusions: </strong>The proposed sequencing optimization framework addresses the temporal mismatch between static SPArc planning and dynamic delivery in synchrocyclotron-based systems. By improving delivery accuracy without compromising plan quality or delivery efficiency, the framework demonstrates the feasibility of incorporating machine-specific delivery timing into dynamic proton arc therapy.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70658"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving portability of knowledge-based planning using an LLM-driven plan refinement framework in lung radiotherapy.","authors":"Zipai Wang, Hao Guo, Yang Lei, Robert Samstein, Kenneth E Rosenzweig, Ming Chao, Tian Liu, Junyi Xia, Jiahan Zhang","doi":"10.1002/mp.70657","DOIUrl":"10.1002/mp.70657","url":null,"abstract":"<p><strong>Background: </strong>Knowledge-based planning (KBP) has improved the quality and efficiency of radiotherapy treatment planning. However, its broader clinical adoption remains limited because effective deployment often requires institution-specific model training and tuning. Publicly available KBP models provide a convenient starting point but may not consistently meet local clinical objectives across institutions.</p><p><strong>Purpose: </strong>We developed and evaluated the Planning Copilot, a large language model (LLM)-guided plan refinement framework designed to operate as a model-agnostic post-processing layer for KBP.</p><p><strong>Methods: </strong>The Planning Copilot is a closed-loop, multi-agent system that iteratively refines KBP-generated plans through structured dosimetric feedback and the selection of clinically validated optimization actions within a treatment planning system. For each case, an initial step-and-shoot IMRT plan was generated with each of three RapidPlan models, including a publicly available model and two institutional models with different optimization constraints. To assess whether the refinement depends on KBP, we additionally evaluated PlanningCopilot starting from a non-KBP fixed objective template applied identically to all cases. The PlanningCopilot was applied without model-specific tuning to 62 retrospective locally advanced NSCLC cases. Clinical goal achievement rates and clinically relevant dose-volume metrics were compared between the initial KBP plans and the refined plans.</p><p><strong>Results: </strong>Across all three KBP models, the PlanningCopilot substantially improved plan quality. Clinical goal achievement increased from 79% to 98% for the UCSD model, from 73% to 97% for Institutional T1, and from 69% to 98% for Institutional T2. Starting from the non-KBP fixed template, the achievement rate increased from 68% to 97%, comparable to the KBP initializations. Significant reductions were observed in key lung dose metrics, including lung Dmean across all models and lung V20 in the Institutional T1 model and template initializations, while target coverage and doses to critical structures were maintained. Notably, the KBP model that prioritized OAR sparing, which exhibited the lowest initial pass rate, showed the highest rescue rate after refinement.</p><p><strong>Conclusions: </strong>An LLM-guided refinement layer can improve the success rate and portability of KBP across heterogeneous models without retraining the underlying KBP system. This approach provides a practical strategy to enhance the reliability of KBP and supports the use of off-the-shelf models through automated, model-agnostic post-processing.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70657"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modal direct LINAC parameter prediction for pancreatic VMAT: An optimization-free approach.","authors":"Zixu Guan, Yukine Shimizu, Takahiro Iwai, Michio Yoshimura, Takashi Mizowaki, Mitsuhiro Nakamura","doi":"10.1002/mp.70656","DOIUrl":"10.1002/mp.70656","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Pancreatic cancer Volumetric Modulated Arc Therapy (VMAT) planning presents a significant dosimetric challenge due to the high-dose gradients required to spare adjacent, radiosensitive organs at risk (OARs) like the stomach and duodenum. This anatomical complexity has limited the scope of automated planning for this site. Broadly, while deep learning (DL) has been introduced to streamline treatment planning, most existing models only predict intermediate outputs, such as dose distributions or fluence maps, which still necessitate a subsequent, computationally expensive inverse optimization step on a treatment planning system.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To address these challenges, we aim to develop an optimization-free fully automated VMAT planning framework for pancreatic cancer. As a key component of this system, this study introduces a DL model designed to directly generate machine parameters from dose distributions and anatomical contours.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 200 vmat plans for pancreatic cancer (prescription: 42 Gy in 15 fractions) were retrospectively collected. The dataset was randomly split into training (n = 170), validation (n = 10), and testing (n = 20) sets. The proposed Multi-modal, Attention & Transformer-Enhanced U-Net (MATE-UNet) utilizes beam's-eye-view (BEV) projections of the reference dose distribution and anatomical contours to directly predict machine-executable multi-leaf collimator (MLC) apertures and Monitor Units (MUs). The proposed model was benchmarked against baseline U-Nets (using contour-only, dose-only, and combined inputs) on the testing set. Model accuracy was assessed using the Dice Similarity Coefficient (DSC) for MLC and Mean Absolute Error (MAE) for MU, while plan quality was evaluated using clinical dose-volume histogram (DVH) metrics and Conformity Index (CI), Homogeneity Index (HI), and Gradient Index (GI).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;MATE-UNet achieved a clinical acceptance rate of 100% (20/20), compared to 70% for the best-performing baseline model. In terms of prediction accuracy, the proposed model achieved a DSC of 0.9553 ± 0.0042 for MLC apertures and an MAE of 1.960 ± 0.396 for MU. Dosimetric evaluation demonstrated that, relative to the reference plans, MATE-UNet maintained comparable target coverage, CI (0.773), and HI (0.100), while achieving a significantly improved GI (3.732, p &lt; 0.05). Furthermore, MATE-UNet significantly reduced the V&lt;sub&gt;39Gy&lt;/sub&gt; of the stomach and duodenum, as well as the global maximum dose, compared with the baseline models (p &lt; 0.05).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates the feasibility of MATE-UNet for the direct prediction of VMAT machine parameters without iterative optimization. By leveraging multi-modal BEV inputs and a Transformer-enhanced architecture, the proposed framework represents a valuable step toward bridging the gap between dose distributions and clinically usable tre","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70656"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT texture phantom dataset with paired image quality assessments for quantitative imaging.","authors":"Morgan A Daly, John M Hoffman, Andrew M Hernandez, Ali Uneri, Bino A Varghese, Joshua Levy, Michael F McNitt-Gray","doi":"10.1002/mp.70633","DOIUrl":"10.1002/mp.70633","url":null,"abstract":"<p><p>To establish a public repository of computed tomography (CT) texture phantom images paired with objective 3D image quality measurements from multiple scanner models using a diverse range of imaging protocols, facilitating investigations into relationships between image quality and quantitative imaging features. Three specialized CT phantoms were scanned: (1) the Corgi^® phantom for image quality assessment, (2) a radiomics liver phantom, and (3) an open-source 3D-printed texture phantom. Image quality assessment included measurement of contrast-to-noise ratio, 3D modulation transfer function, and 3D noise power spectrum. Data were acquired on four CT scanner models from two manufacturers at five <math><msub><mi>CTDI</mi> <mrow><mi>v</mi> <mi>o</mi> <mi>l</mi></mrow> </msub> </math> levels (2.05-17.11 mGy) and reconstructed with eight different kernels, yielding 160 total conditions (combinations of scanner, dose, kernel). Data were validated for integrity and completeness, resulting in the exclusion of six conditions. The paired texture phantom and image quality (PTP-IQ) dataset includes: (1) DICOM image series of two texture phantoms acquired across the 154 conditions, as well as (2) image quality metrics derived from each corresponding set of scanner, acquisition and reconstruction settings provided in HDF5 format. This dataset enables the development and validation of harmonization methods for multi-center quantitative imaging studies, investigation of protocol-dependent QIF variability, and optimization of acquisition protocols for radiomics applications. The controlled and systematic study design facilitates isolation of individual protocol effects on quantitative measurements.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70633"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and performance evaluation of an upright dedicated cone-beam breast CT system.","authors":"Thomas C Larsen, Hsin Wu Tseng, William Ross, Stephen Araujo, Pengwei Wu, Eri Haneda, Cynthia Davis, Andrew Karellas, Srinivasan Vedantham","doi":"10.1002/mp.70664","DOIUrl":"10.1002/mp.70664","url":null,"abstract":"<p><strong>Background: </strong>Dedicated breast CT is an emerging breast X-ray imaging modality. While current commercial breast CT systems use prone-patient, pendant-breast geometry, the system described here uses upright patient geometry with the uncompressed breast supported by a cup.</p><p><strong>Purpose: </strong>The purpose of this work is to describe the development of a newly designed, upright geometry, dedicated cone-beam breast CT system and to evaluate its imaging performance using objective metrics.</p><p><strong>Methods: </strong>The prototype system uses a tungsten-target, mammography-format, X-ray tube operating at 60 kV with 0.25 mm Cu and 1 mm Al added filtration, and a complementary metal-oxide semiconductor (CMOS) detector with 0.152 mm pixel pitch coupled to 500 microns thick CsI:Tl scintillator. During short scan acquisition, the X-ray source moves inferior to the breast, and 210 projections are acquired over an angular range of 210 degrees. The projections are reconstructed to an isotropic voxel pitch of 0.22 mm using Feldkamp-Davis-Kress (FDK) algorithm with Parker weights. Quantitative performance measures including linearity, modulation transfer function (MTF), and noise power spectrum (NPS) were evaluated. Phantom studies were conducted at various X-ray tube current (mA) and pulse-width (ms) combinations with the objective of determining the minimum detectable size of low-contrast targets and calcium carbonate spheres representing soft tissue lesions and microcalcification clusters, respectively.</p><p><strong>Results: </strong>The measured 1st HVL was 4.23 ± 0.01 mm of Al. The limiting resolution (10% MTF) was 2.18 mm^-1 in the coronal (cross-sectional) plane near the axis of rotation. In the coronal plane, the peak of the NPS occurred at 0.5 mm^-1. Phantom studies at a mean glandular dose of 3-5.7 mGy showed the ability to visualize 2-3 mm low-contrast targets and 0.27-0.29 mm calcium carbonate spheres.</p><p><strong>Conclusions: </strong>The developed upright breast CT system showed the ability to achieve high spatial resolution and low contrast resolution. The excellent technical performance of the breast CT system reported here suggests that further investigations using patient imaging are warranted.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70664"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomy-aware fusion attention network for high-precision prostate MRI segmentation.","authors":"Yulong Wang, Hui Huang, Yan Ma, Hongqing Zhu, Chunlin Wen, Feiniu Yuan, Bingcang Huang","doi":"10.1002/mp.70669","DOIUrl":"10.1002/mp.70669","url":null,"abstract":"<p><strong>Background: </strong>Accurate prostate segmentation in Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and treatment. However, it remains a challenging task due to low soft-tissue contrast and structural ambiguity in the gland's appearance. While recent deep learning methods have focused on refining network structures, they often neglect the inherent anatomical consistency of the prostate.</p><p><strong>Purpose: </strong>To address this limitation, a novel segmentation framework integrating an Anatomy-Aware Fusion Attention (AAFA) module is proposed.</p><p><strong>Methods: </strong>By leveraging anatomical templates, our approach establishes a multilevel feature cross-attention mechanism that enhances global contextual modeling of prostate regions. Additionally, we introduce a Phased Learning strategy that progressively trains the model to mitigate the adverse effects of invalid or noisy samples commonly found in clinical MRI data.</p><p><strong>Results: </strong>Extensive ablation studies on the PROMISE12 dataset validate the contribution of each component to overall performance. Comparison experiments on both PROMISE12 and MSD Prostate datasets show that our method consistently outperforms existing approaches across key metrics, such as Dice similarity coefficient (DSC), Intersection over Union (IoU), Precision and 95% Hausdorff distance (HD95).</p><p><strong>Conclusions: </strong>These results confirm the robustness and strong generalization capability of the proposed framework in challenging clinical segmentation tasks.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 9","pages":"e70669"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13542788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}