Medical physics最新文献

{"title":"A virtual imaging study of microcalcification detection performance in digital breast tomosynthesis: Patients versus 3D textured phantoms.","authors":"Katrien Houbrechts, Lesley Cockmartin, Nicholas Marshall, Liesbeth Vancoillie, Stoyko Marinov, Ruben Sanchez de la Rosa, Remy Klausz, Ann-Katherine Carton, Hilde Bosmans","doi":"10.1002/mp.17873","DOIUrl":"https://doi.org/10.1002/mp.17873","url":null,"abstract":"<p><strong>Background: </strong>Clinical studies to evaluate the performance of new imaging devices require the collection of patient data. Virtual methods present a potential alternative in which patient-simulating phantoms are used instead.</p><p><strong>Purpose: </strong>This work uses a virtual imaging technique to examine the extent to which human observer microcalcification detection performance in phantom backgrounds matches that in real patient backgrounds for digital breast tomosynthesis (DBT).</p><p><strong>Methods: </strong>This work used the following DBT image datasets: (1) 142 real patient images and (2) 20 real images of the physical L1 phantom, both acquired on a GEHC Senographe Pristina system; (3) 217 simulated images of the Stochastic Solid Breast Texture (SSBT) phantom and (4) 217 simulated images of the digital L1 phantom, both created with the CatSim framework. The L1 phantom is a PMMA container filled with water and PMMA spheres of varying diameters. The SSBT phantom is a computational phantom composed of glandular and adipose tissue compartments. Signal-present images were generated by inserting simulated microcalcification clusters, containing individual calcifications with thicknesses and projected areas in the range of 165-180 µm, 195-210 µm and 225-240 µm, and 0.025-0.031 mm<sup>2</sup>, 0.032-0.040 mm<sup>2</sup>, 0.041-0.045 mm<sup>2</sup> respectively, at random locations into all four background types. Three human observers performed a search/localization task on 120 signal-present and 97 signal-absent volumes of interest (VOIs) per background type. A jackknife alternative free-response receiver operating characteristic (JAFROC) analysis was applied to calculate the area under the curve (AUC). The simulation procedure was first validated by testing the physical and digital L1 background AUC values for equivalence (margin = 0.1). The AUC for patient backgrounds and each phantom type (SSBT, physical L1, digital L1) was then compared. Additionally, each patient's VOI was categorized in homogeneous or heterogeneous background texture distribution by an experienced physicist, and by local volumetric breast density (VBD) at the insertion position to examine their effect on correctly detected fraction of microcalcification clusters.</p><p><strong>Results: </strong>Mean AUC for the patient images was 0.70 ± 0.04, while mean AUCs of 0.74 ± 0.04, 0.76 ± 0.03, and 0.76 ± 0.07 were found for the SSBT, physical L1 and digital L1 phantoms, respectively. The AUC for the physical and digital L1 phantoms was equivalent (p = 0.03), as well as for the patients and SSBT backgrounds (p = 0.002). The physical and digital L1 images did not have equivalent detection performance compared to patient images (p = 0.06 and p = 0.9, respectively). In patient backgrounds, the correctly detected fraction of microcalcifications clusters fell from 0.53 for the lowest density (VBD < 4.5%) to 0.40 for the highest density (VBD ≥ 15.5%). Microcalcifica","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learned high resolution energy-integrating detector CT angiography: Harnessing the power of ultra-high-resolution photon counting detector CT.","authors":"Emily K Koons, Shaojie Chang, Andrew D Missert, Hao Gong, Jamison E Thorne, Safa Hoodeshenas, Prabhakar Shantha Rajiah, Cynthia H McCollough, Shuai Leng","doi":"10.1002/mp.17874","DOIUrl":"https://doi.org/10.1002/mp.17874","url":null,"abstract":"<p><strong>Background: </strong>Coronary computed tomography angiography (cCTA) is a widely used noninvasive diagnostic exam to assess patients for coronary artery disease (CAD). However, the spatial resolution of most CT scanners is limited due to the use of energy-integrating detectors (EIDs).</p><p><strong>Purpose: </strong>To develop a convolutional neural network (Improved LUMEN visualization through Artificial super-resoluTion imagEs (ILUMENATE)) informed by photon-counting-detector (PCD)-CT to improve EID-CT image resolution and determine its impact on cCTA.</p><p><strong>Materials and methods: </strong>With IRB approval, 30 patients undergoing clinically indicated cCTA were scanned with EID-CT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and subsequently with ultra-high-resolution (UHR) PCD-CT (NAEOTOM Alpha, Siemens Healthineers) on the same day. ILUMENATE was trained on eight patient PCD-CT datasets (67,890 patch pairs with 90% for training (61,101), 10% reserved for validation (6,789)) and applied to 22 unseen EID-CT cases. Spatial resolution was evaluated using line profiles and percent diameter stenosis quantified with a severity score assigned. Two experienced radiologists, blinded to image type, selected preferred series and scored images for overall quality, sharpness, and noise comparing original EID-CT and ILUMENATE output.</p><p><strong>Results: </strong>Visual assessment and line profiles showed substantial resolution improvement with ILUMENATE. Percent diameter stenosis was significantly reduced (mean ± standard deviation: 4.42% ± 4.82%) using ILUMENATE (p < 0.001) with nine lesions shifting down in severity score. Readers preferred ILUMENATE images in 22/22 cases and scored ILUMENATE superiorly for overall quality, sharpness, and noise (p < 0.05).</p><p><strong>Conclusions: </strong>ILUMENATE enhanced image resolution, resulting in improved overall image quality, reduced calcium blooming artifacts, and improved lumen visibility in cCTA exams performed using EID-CT. This could potentially allow for improved accessibility to UHR image quality, allowing for more accurate assessment of CAD.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144016096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of a novel pencil beam scanning Bragg peak FLASH technique to a commercial treatment planning system.","authors":"Alexander Bookbinder, Miriam Krieger, Pierre Lansonneur, Anthony Magliari, Xingyi Zhao, J Isabelle Choi, Charles B Simone, Haibo Lin, Michael Folkerts, Minglei Kang","doi":"10.1002/mp.17876","DOIUrl":"https://doi.org/10.1002/mp.17876","url":null,"abstract":"<p><strong>Background: </strong>Ultra-high dose rate, or FLASH, radiotherapy has shown promise in preclinical experiments of sparing healthy tissue without compromising tumor control. This \"FLASH effect\" can compound with dosimetric sparing of the proton Bragg peak (BP) using a method called Single Energy Pristine Bragg Peak (SEPBP) FLASH. However, this and other proposed FLASH techniques are constrained by lack of familiar treatment planning systems (TPSs). Creating modules to implement SEPBP FLASH into a commercial TPS opens up the possibility of more widespread investigation of FLASH and lays the groundwork for future clinical translation.</p><p><strong>Purpose: </strong>To implement, investigate, and benchmark the capacity of a commercial TPS research extension for BP FLASH SBRT treatment planning by studying the dosimetric properties and FLASH ratio for critical organs-at-risk (OARs) at several sites.</p><p><strong>Methods: </strong>A 250 MeV clinical proton beam model was commissioned in the Eclipse TPS (Varian Medical Systems, Palo Alto, USA). BP FLASH fields were single-layer maximum-energy beams with a universal range shifter (URS) and field-specific range compensators (RCs). RCs for each beam angle were included as contours within the structure set, while the URS was modeled in the PBS beamline. Spotmaps were created using Lloyd's algorithm with minimum monitor units (MU)-based spacing to ensure plan quality and preserve FLASH coverage for critical OARs. Inverse optimization while preserving minimum MU constraints was done with scorecard-based optimization. Fifteen SBRT cases from three anatomical sites (liver, lung, base-of-skull [BOS]) previously treated at the New York Proton Center were re-optimized using this method, and dosimetric characteristics of BP plans were compared to clinically treated plans. FLASH ratios for critical OARs were evaluated for BP FLASH plans.</p><p><strong>Results: </strong>The dose distributions, including target uniformity, conformity index (CI), and DVHs, showed no significant difference in clinically-used metrics between BP FLASH and clinically delivered plans across all anatomical sites. Mean 40 Gy/s FLASH ratios for critical OARs were above 84% for all but one OAR with 2 Gy threshold and above 98% for all OARs with 5 Gy threshold. D_max for liver and BOS cases was 111.3 ± 2.68 and 112.88 ± 1.29, respectively, and D_2% for lung cases was 112.04 ± 1.09. All D_max remained below 115%.</p><p><strong>Conclusions: </strong>Inverse planning using a single-energy BP FLASH technique based on sparse spots and ultra-high minimum MU/spot can achieve intensity-modulated proton therapy (IMPT)-equivalent quality and sufficient FLASH coverage. This successful prototype brings us closer to commercial implementation and may increase the availability of proton FLASH dosimetry studies.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing the evaluation of radiation-induced vaginal toxicity using ultrasound radiomics: Phantom validation and pilot clinical study.","authors":"Jing Wang, Joseph Shelton, Boran Zhou, Deborah C Marshall, Himanshu Joshi, Emi J Yoshida, Xiaofeng Yang, Tian Liu","doi":"10.1002/mp.17864","DOIUrl":"https://doi.org/10.1002/mp.17864","url":null,"abstract":"<p><strong>Background: </strong>Radiation-induced long-term toxicities, such as vaginal stenosis, severely impact the quality of life for patients undergoing pelvic radiotherapy (RT) for gynecologic (GYN) malignancies. However, current methods for assessing these toxicities rely on subjective physical examinations and patient-reported symptoms, leading to inconsistencies in grading and suboptimal management.</p><p><strong>Purpose: </strong>This pilot study investigates the potential of ultrasound-based radiomics, specifically gray level co-occurrence matrix (GLCM) texture metrics, as objective and quantitative biomarkers for evaluating long-term radiation-induced vaginal toxicity.</p><p><strong>Methods: </strong>A two-phase study was conducted. First, a phantom study was performed to identify robust GLCM texture features with low variability [coefficient of variance (COV) < 10%] across ultrasound brightness settings. In a subsequent clinical pilot study, 22 female participants were recruited: 10 had received pelvic radiotherapy (RT) with follow-up times ranging from 8 to 23 months, while 12 served as non-RT controls. All participants underwent transvaginal ultrasound imaging, and GLCM texture features were extracted for analysis. A Mann-Whitney U test was used to assess between-group differences of distribution, with a p value < 0.05 identified as statistically significance. Cohen's d values were calculated to quantify effect sizes, with a value of greater than 0.8 indicating large effects.</p><p><strong>Results: </strong>Seventeen GLCM features demonstrated robustness (COVs < 10%) across brightness settings in the phantom study, including two with COVs < 1%, 10 with COVs between 1% and 5%, and five with COVs between 5% and 10%. In the clinical study, four texture features showed significant differences between the treated group and controls (p < 0.05). Specifically, the treated group exhibited a 15.5% increase in correlation (p = 0.03), a 35.8% decrease in contrast (p = 0.03), a 10.1% decrease in difference entropy (p = 0.04), and a 17.9% decrease in dissimilarity (p = 0.07).</p><p><strong>Conclusion: </strong>This phantom and pilot study demonstrated that ultrasound GLCM features can serve as reliable quantitative biomarkers for assessing radiation-induced vaginal toxicity in female patients receiving pelvic RT for GYN cancers. Implementing these biomarkers in clinical practice could enhance the objectivity of toxicity evaluations, leading to more consistent grading and better-informed follow-up care for patients.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small-field output factor dependence on the field size definition in MR-Linac.","authors":"Indra J Das, Ahtesham U Khan, Poonam Yadav","doi":"10.1002/mp.17857","DOIUrl":"https://doi.org/10.1002/mp.17857","url":null,"abstract":"<p><strong>Background: </strong>Radiation beam characteristics are primarily evaluated based on field size. However, in small fields, especially with magnetic fields used in new technology (MR-Linac), the field size definition is altered. Typically, field size is defined by two methods: geometric and dosimetric, which are evaluated in this study.</p><p><strong>Purpose: </strong>Small field size definitions are distorted due to lateral electron disequilibrium and the presence of magnetic fields. MR-Linac systems, which combine an MR imaging system and a linear accelerator on a single gantry, require precise evaluations of field size definitions and beam parameters, particularly for small fields. which is investigated in this study.</p><p><strong>Methods: </strong>A 0.35 T MRIdian Viewray system was evaluated using beam profiles and field output factors (FOF) with various MR-compatible microdetectors, such as ion chamber, microDiamond, microSilicon, and plastic scintillators. Validity of geometric field size (S) and dosimetric field size (S_clin) is investigated with measurements performed with MR compatible scanning water phantom at 85 cm source-to-surface distance (SSD) at a depth of 5 cm. Measured FOF data was compared with treatment planning systems (TPS) and independent Monte Carlo simulations.</p><p><strong>Results: </strong>The measured S_clin data is detector and machine-dependent, while S is machine-dependent only. The FOF was found to be a smooth function of S within experimental uncertainties, showing higher reproducibility compared to S_clin which exhibited erratic behavior.</p><p><strong>Conclusions: </strong>It is concluded that geometric field size (S) provides accurate beam characterization data, whereas S_clin may not be a reliable parameter in MR-Linac systems.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A carbon ion minibeam treatment planning method with scissor beams.","authors":"Wei Wu, Weijie Zhang, Jiaxin Li, Wei Wang, Yuting Lin, Qiang Li, Hao Gao","doi":"10.1002/mp.17869","DOIUrl":"https://doi.org/10.1002/mp.17869","url":null,"abstract":"<p><strong>Background: </strong>Minibeam radiation therapy (MBRT) employs a highly modulated spatial dose distribution characterized by the peak-to-valley dose ratio (PVDR). Carbon minibeam radiation therapy (cMBRT) offers higher PVDR and relative biological effectiveness (RBE) compared to proton MBRT. However, achieving uniform target dose (UTD) coverage while maintaining a high PVDR in organs at risk (OAR) remains challenging.</p><p><strong>Purpose: </strong>To address this challenge and optimize the balance between PVDR in OAR and target dose homogeneity, we introduce the scissor-beam (SB) approach for cMBRT.</p><p><strong>Methods: </strong>The SB method introduces scissor beam (SB) splitting, where each original beam is divided into a primary beam and a complementary beam. The primary beam maintains the same angle as the original beam, while the complementary beam is rotated by a small degree. This rotation angle is determined based on the center-to-center distance and the relative positions of the OAR and the target. Monte Carlo simulation using GATE/GEANT4 were performed for dose calculations. The effectiveness of SB was evaluated in comparison to conventional cMBRT method (MB) and crossfire (CF) method in terms of target dose uniformity, OAR sparing, and PVDR in OAR across three clinical cases: lung, pancreas, and head-and-neck (HN) cancers.</p><p><strong>Results: </strong>Compared to MB (2 mm center to center distance(d_ctc)), SB increased OAR PVDR by 150% and matched the PVDR of MB (4 mm d_ctc) with ≤5% difference across lung, pancreas, and head and neck (HN) cases. SB improved target conformity (CI) by 118%-167% over MB (4 mm d_ctc), reducing lung D_mean by 12%-27%, liver D_mean by 18%, and brainstem/spinal cord D_max by 42%-54%. Relative to CF, SB maintained similar PVDR (≤4% difference) while enhancing OAR sparing: 33% lower left lung D_mean, 71% reduced kidney D_mean, and complete spinal cord sparing (pancreas) and HN cases saw 37% lower brainstem D_max with SB. These results highlight the effectiveness of the SB method in achieving better target dose uniformity and OAR sparing while maintaining comparable PVDR values.</p><p><strong>Conclusions: </strong>We have proposed a novel SB method for cMBRT. SB provides a better balance between UTD and PVDR in OAR compared to MB. Additionally, SB demonstrates superior OAR protection compared to CF. This innovative approach holds significant potential to enhance the therapeutic ratio of cMBRT, offering improved treatment outcomes and reduced risks for patients.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A segmentation method for oral CBCT image based on Segment Anything Model and semi-supervised teacher-student model.","authors":"Jianhong Gan, Runqing Kang, Xun Deng, Tongli He, Nie Yu, Yuling Gan, Peiyang Wei, Xiangyi Chen, Xiaoli Peng, Zhibin Li","doi":"10.1002/mp.17854","DOIUrl":"https://doi.org/10.1002/mp.17854","url":null,"abstract":"<p><strong>Background: </strong>Accurate segmentation of oral cone beam computed tomography (CBCT) images is essential for research and clinical diagnosis. However, irregular and blurred tooth boundaries in CBCT images complicate the labeling of oral tissues, and insufficient labeled samples further limit the generalization ability of segmentation models. The Segment Anything Model (SAM) demonstrates strong generalization and segmentation accuracy across diverse tasks as a vision foundation model. The Teacher-Student (TS) model has proven effective in semi-supervised learning approaches.</p><p><strong>Purpose: </strong>To accurately segment various parts of oral CBCT, such as enamel, pulp, bone, blood vessels, air, etc., an improved segmentation method named SAM-TS is proposed, which combines SAM with the TS model. SAM-TS leverages Low-Rank Adaptation (LoRA) to fine-tune the SAM model on oral CBCT images with fewer parameters.</p><p><strong>Methods: </strong>To efficiently utilize numerous unlabeled images for training models, the LoRA strategy is improved to fine-tune the SAM. The fine-tuned SAM and teacher models collaboratively generate pseudo-labels on unlabeled images, which are filtered and utilized to train the student model. Then, a data augmentation-based Mean Intersection over Union (MIoU) method is proposed to filter out unreliable or spurious pseudo-labels. Finally, the Exponential Moving Average (EMA) method is used to transfer the student model's parameters to the teacher model. After repeating this process, the final optimized student model for segmentation is obtained. The experimental results demonstrate that incorporating unlabeled data into model training through SAM-TS significantly enhances the model's generalization ability and segmentation accuracy.</p><p><strong>Results: </strong>Compared to the baseline algorithm, the proposed method achieves an overall improvement of over 6.48% in MIoU. In the tooth segmentation task, the minimum MIoU and maximum MIoU increased by at least 10% and 27.32%, respectively. In the bone segmentation task, the minimum MIoU and maximum MIoU increased by 7.9% and 32.44%, respectively. Additionally, for overall segmentation, the Hausdorff distance (HD) decreased by 5.1 mm, and the Dice coefficient increased by 2.87%.</p><p><strong>Conclusion: </strong>SAM-TS outperforms existing semi-supervised methods, offering a more competitive and efficient approach to CBCT image segmentation. This method addresses the data annotation bottleneck and opens new avenues for semi-supervised learning applications in medical imaging.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient method to evaluate the mechanical accuracy of MR-Linac configured with an MV panel.","authors":"Zhihui Hu, Hui Yan, Ke Zhang, Peng Huang, Yuan Xu, Jianrong Dai, Kuo Men","doi":"10.1002/mp.17843","DOIUrl":"https://doi.org/10.1002/mp.17843","url":null,"abstract":"<p><strong>Background: </strong>The mechanical accuracy of magnetic resonance linear accelerators (MR-Linac) is crucial in terms of the accuracy of magnetic resonance-guided radiotherapy. Current clinical quality assurance procedures, which involve individual measurements of the accelerator's mechanical components, have high time costs.</p><p><strong>Purpose: </strong>This study developed an efficient method to evaluate the mechanical accuracy of the MR-Linac by measuring multiple mechanical components simultaneously using a single phantom with only one setup.</p><p><strong>Methods: </strong>The measurements were performed using an MR-to-MV phantom with an Elekta Unity MR-Linac. The phantom contains regularly arranged ceramic ball bearings (BB) that are visible in megavoltage (MV) images. MV projection images of the phantom were acquired at various gantry angles, and a software program was developed to detect the radiation field edges and the positions of the BBs, thereby enabling effective and efficient measurement of the radiation isocenter size, field size accuracy, couch position accuracy, and gantry angle accuracy. The accuracy, reproducibility and robustness of the proposed method were evaluated through tests with different gantry angles and phantom offsets.</p><p><strong>Results: </strong>The entire measurement procedure was completed in 6.3 ± 0.2 min, and the obtained results were consistent with those of conventional methods. The proposed method can detect angular uncertainties as small as 0.1°. The measurement results exhibited excellent inter-operator reproducibility, with the intraclass correlation coefficient >0.9 and standard deviations within 0.1 mm and 0.1°. In the robustness test, introducing a 2 mm phantom setup error resulted in an average deviation of 0.02 mm in the measured radiation isocenter size and a maximum deviation of approximately 0.1° in the gantry angle.</p><p><strong>Conclusion: </strong>The proposed is a simple, robust, and accurate tool to measure the mechanical accuracy of an MR-Linac. By enabling simultaneous measurement of multiple mechanical parameters using a single phantom, the proposed method reduces the time costs of quality assurance procedures considerably.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling inter-reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model.","authors":"Yafei Dong, Thibault Marin, Yue Zhuo, Elie Najem, Arnaud Beddok, Laura Rozenblum, Maryam Moteabbed, Kira Grogg, Fangxu Xing, Jonghye Woo, Yen-Lin E Chen, Ruth Lim, Xiaofeng Liu, Chao Ma, Georges El Fakhri","doi":"10.1002/mp.17865","DOIUrl":"https://doi.org/10.1002/mp.17865","url":null,"abstract":"<p><strong>Background: </strong>Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter-reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case.</p><p><strong>Purpose: </strong>This study aims to develop a deep learning-based technique to generate multiple CTV contours for each case, simulating the inter-reader variability in the clinical practice.</p><p><strong>Methods: </strong>We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG-PET), x-ray computed tomography (CT), and pre-contrast T1-weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi-modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model-based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter-reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation.</p><p><strong>Results: </strong>The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state-of-the-art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state-of-the-art models). It also achieved the second-highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation.</p><p><strong>Conclusions: </strong>The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter-reader variability in CTV delineation.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.","authors":"Jaryd R Christie, Perrin Romine, Karen Eddy, Delphine L Chen, Omar Daher, Mohamed Abdelrazek, Richard A Malthaner, Mehdi Qiabi, Rahul Nayak, Paul Kinahan, Viswam S Nair, Sarah A Mattonen","doi":"10.1002/mp.17862","DOIUrl":"https://doi.org/10.1002/mp.17862","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studies have shown that a high proportion of these patients will experience a recurrence after their resection, leading to an increased risk of death. Cancer staging is currently the gold standard for establishing a patient's prognosis and can help clinicians determine patients who may benefit from additional therapy. However, medical images which are used to help determine the cancer stage, have been shown to hold unutilized prognostic information that can augment clinical data and better identify high-risk NSCLC patients. There remains an unmet need for models to incorporate clinical, pathological, surgical, and imaging information, and extend beyond the current staging system to assist clinicians in identifying patients who could benefit from additional therapy immediately after surgery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;We aimed to determine whether a deep learning model (DLM) integrating FDG PET and CT imaging from the thoracic cavity along with clinical, surgical, and pathological information can predict NSCLC recurrence-free survival (RFS) and stratify patients into risk groups better than conventional staging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;Surgically resected NSCLC patients enrolled between 2009 and 2018 were retrospectively analyzed from two academic institutions (local institution: 305 patients; external validation: 195 patients). The thoracic cavity (including the lungs, mediastinum, pleural interfaces, and thoracic vertebrae) was delineated on the preoperative FDG PET and CT images and combined with each patient's clinical, surgical, and pathological information. Using the local cohort of patients, a multi-modal DLM using these features was built in a training cohort (n = 225), tuned on a validation cohort (n = 45), and evaluated on testing (n = 35) and external validation (n = 195) cohorts to predict RFS and stratify patients into risk groups. The area under the curve (AUC), Kaplan-Meier curves, and log-rank test were used to assess the prognostic value of the model. The DLM's stratification performance was compared to the conventional staging stratification.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The multi-modal DLM incorporating imaging, pathological, surgical, and clinical data predicted RFS in the testing cohort (AUC = 0.78 [95% CI:0.63-0.94]) and external validation cohort (AUC = 0.66 [95% CI:0.58-0.73]). The DLM significantly stratified patients into high, medium, and low-risk groups of RFS in both the testing and external validation cohorts (multivariable log-rank p &lt; 0.001) and outperformed conventional staging. Conventional staging was unable to stratify patients into three distinct risk groups of RFS (testing: p = 0.94; external validation: p = 0.38). Lastly, the DLM displayed the ability to further stratify patients significantly into sub-risk groups with","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}