Physica Medica-European Journal of Medical Physics最新文献

{"title":"CAD-based Monte Carlo dose calculation system for evaluating geometrical effect of inserted materials in carbon-ion radiation therapy","authors":"Min Cheol Han,&nbsp;Yongdo Yun,&nbsp;Taeho Kim,&nbsp;Soorim Han,&nbsp;Changhwan Kim,&nbsp;Dong Wook Kim,&nbsp;Ho Lee,&nbsp;Hojin Kim,&nbsp;Chae-Seon Hong,&nbsp;Jin Sung Kim","doi":"10.1016/j.ejmp.2025.105051","DOIUrl":"10.1016/j.ejmp.2025.105051","url":null,"abstract":"<div><h3>Purpose</h3><div>Carbon-ion radiation therapy (CIRT) achieves potent tumor control by leveraging the unique physical and biological properties of carbon ions, such as the Bragg peak and high relative biological effectiveness. However, the presence of implanted markers or embolization coils can alter the beam range; therefore, clinical planning becomes complex. This study developed and validated a computer-aided design (CAD)-based Monte Carlo (MC) dose calculation system to accurately assess the geometric effects of materials inserted in CIRT.</div></div><div><h3>Methods</h3><div>A gold fiducial marker, typically used for prostate CIRT, was evaluated in both experimental and simulation settings. Gafchromic™ EBT3 films, placed at multiple depths in a solid–water phantom, were used to measure the dose distributions. Simultaneously, a Tornado Embolization Microcoil™ (Cook Medical) for hepatic transcatheter arterial chemoembolization was modeled using CAD and simulated by considering different orientations. MC simulations were performed using TOol for PArticle Simulation, with the beam parameters obtained from the Heavy Ion Therapy Center used for validation.</div></div><div><h3>Results</h3><div>The film-based and MC-based dose profiles showed a similar range shift for the fiducial marker, despite the linear energy-transfer dependence of the films. The orientation of the microcoil slightly affected the range shift (∼0.1 mm). Moreover, CAD-based modeling demonstrated a more accurate representation than using simplified geometries.</div></div><div><h3>Conclusions</h3><div>The developed CAD-based MC simulation system is reliable and practical for evaluating the dosimetric impact of implanted materials on CIRT. Although MC simulations require extended computational time, the ability to incorporate absolute dose data and precisely model complex structures enhances the confidence in treatment planning.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105051"},"PeriodicalIF":3.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty and normalized glandular dose evaluations in digital mammography and digital breast tomosynthesis with a machine learning methodology","authors":"Antonio Sarno ,&nbsp;Rodrigo T. Massera ,&nbsp;Gianfranco Paternò ,&nbsp;Paolo Cardarelli ,&nbsp;Nicholas Marshall ,&nbsp;Hilde Bosmans ,&nbsp;Kristina Bliznakova","doi":"10.1016/j.ejmp.2025.105043","DOIUrl":"10.1016/j.ejmp.2025.105043","url":null,"abstract":"<div><h3>Purpose</h3><div>To predict the normalized glandular dose (DgN) coefficients and the related uncertainty in mammography and digital breast tomosynthesis (DBT) using a machine learning algorithm and patient-like digital breast models.</div></div><div><h3>Methodology</h3><div>126 patient-like digital breast phantoms were used for DgN Monte Carlo ground truth calculations. An Automatic Relevance Determination Regression algorithm was used to predict DgN from anatomical breast features. These features included compressed breast thickness, glandular fraction by volume, glandular volume, center of mass and standard deviation of the glandular tissue distribution in the cranio-caudal direction. An algorithm for data imputation was explored to account for avoiding the use of the latter two features.</div></div><div><h3>Results</h3><div>5-fold cross validation showed that the predictive model provides an estimation of DgN with 1% average difference from the ground truth; this difference was less than 3% in 50% of the cases. The average uncertainty of the estimated DgN values was 9%. Excluding the information related to the glandular distribution increased this uncertainty to 17% without inducing a significant discrepancy in estimated DgN values, with half of the predicted cases differing from the ground truth by less than 9%. The data imputation algorithm reduced the estimated uncertainty, without restoring the original performance. Predictive performance improved by increasing tube voltage.</div></div><div><h3>Conclusion</h3><div>The proposed methodology predicts the DgN in mammography and DBT for patient-derived breasts with an uncertainty below 9%. Predicting test evaluations reported 1% average difference from the ground truth, with 50% of the cohort cases differing by less than 5%.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105043"},"PeriodicalIF":3.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of the use of dosimeters for ionizing radiation from fluoroscopy − a systematic literature review and meta-analysis","authors":"Sara Videira ,&nbsp;Matilde Rodrigues ,&nbsp;Joana Santos ,&nbsp;Joana Guedes ,&nbsp;João Martins ,&nbsp;Manuela Vieira da Silva","doi":"10.1016/j.ejmp.2025.105037","DOIUrl":"10.1016/j.ejmp.2025.105037","url":null,"abstract":"<div><div>This study aims to assess the prevalence of individual dosimeter use among workers exposed to ionizing radiation during fluoroscopy-guided procedures. Additionally, factors contributing to its use were identified.</div><div>Studies were identified through searches in five databases on 13 April 2024. Additionally, snowballing techniques were employed. The review followed PRISMA guidelines and the CoCoPop model. A narrative synthesis, bibliometric analysis, and <em>meta</em>-analysis were performed. Study quality was assessed using the Joanna Briggs Institute checklist for prevalence studies.</div><div>Fifty studies involving 11,067 individuals were included. Orthopedics/traumatology was the most studied specialty (46 %). Median use rates were: 24 %(IQR = 44 %) for eye lens dosimeters, 15 %(IQR = 13 %) for electronic real-time dosimeters, 27 %(IQR = 42 %) for wrist/finger dosimeters, 25 %(IQR = 23 %) for collar/thyroid dosimeters and 5 % for ankle dosimeter; 15 %(IQR = 78 %) reported no monitoring device. In 15 studies (30 %) with 4,188 individuals (38 %), the overall prevalence of “always” using whole-body dosimeters was 43 %[95 %CI:24–62]. By continent, the highest prevalence was found in Africa (75 %[95 % CI: 46–95]), while the lowest was in the “Americas” (33 %[95 % CI: 16–52]). Significant moderator effects were found: higher prevalence in “Africa” (p = 0.04), “gastroenterology” (p = 0.04), and “involving radiology” (p = 0.01); lower in “orthopedics” (p = 0.01) and “physicians” (p = 0.03). No significant moderator effects were found: “very high Human Development Index” (p = 0.72) and “high Human Development Index” (p = 0.69). Studies showed moderate risk of bias (6/9), with little evidence of publication bias.</div><div>Exposure doses may be underestimated due to the low prevalence of dosimeter use. Interventions targeting individual and organizational factors are needed to promote consistent use and improve safety.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105037"},"PeriodicalIF":3.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the stability of lung cancer radiotherapy dosiomic features under respiratory motion","authors":"Kai Zhao ,&nbsp;Hui Xu ,&nbsp;Jie Zhang ,&nbsp;Xiao Lin ,&nbsp;Liming Sheng ,&nbsp;Yujin Xu ,&nbsp;Binbing Wang ,&nbsp;Qing Hou ,&nbsp;Xue Bai","doi":"10.1016/j.ejmp.2025.105040","DOIUrl":"10.1016/j.ejmp.2025.105040","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the stability of dosiomic features in response to dose distribution variations caused by respiratory motion.</div></div><div><h3>Methods and Materials</h3><div>A total of 24 lung cancer patients who underwent 4DCT scanning and radiotherapy were included. For each patient, a 3D dose matrix and three 4D dose matrices generated using three distinct methods were calculated. Dosiomic features were extracted from dose distributions for four regions of interest: the gross tumor volume (GTV), the planning target volume (PTV), the heart, and the lung. The stability of each dosiomic feature was evaluated using the coefficient of variation (CV), while reproducibility was assessed using the intraclass correlation coefficient (ICC). The differences between 3D and 4D dose features were determined using the normalized difference (ND).</div></div><div><h3>Results</h3><div>5.0% of dosiomic features had a CV greater than or equal to 20%. The CVs were highest in the GTV region and lowest in the lung region. Additionally, 0.27% of features had an ICC less than 0.5, while 92.74% had an ICC greater than 0.9. 16.48% of features had an absolute value of the ND greater than 0.4.</div></div><div><h3>Conclusions</h3><div>The stability of certain dosiomic features is strongly influenced by respiratory motion in radiotherapy. To ensure result reproducibility, dosiomic studies should fully consider the impact of respiratory motion during dose calculation or excluding unstable features to enhance model stability.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105040"},"PeriodicalIF":3.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tumor control probability in large sacral chordomas treated with carbon ions radiotherapy integrating advanced microstructural modelling","authors":"Giovanni Parrella ,&nbsp;Letizia Morelli ,&nbsp;Silvia Molinelli ,&nbsp;Giuseppe Magro ,&nbsp;Lars Glimelius ,&nbsp;Jakob Ödén ,&nbsp;Mario Ciocca ,&nbsp;Sara Imparato ,&nbsp;Marco Rotondi ,&nbsp;Maria Rosaria Fiore ,&nbsp;Ester Orlandi ,&nbsp;Guido Baroni ,&nbsp;Chiara Paganelli","doi":"10.1016/j.ejmp.2025.105038","DOIUrl":"10.1016/j.ejmp.2025.105038","url":null,"abstract":"<div><h3>Purpose</h3><div>To integrate patient-specific cell count data from diffusion-weighted MRI (DWI) into the linear-quadratic (LQ) Poisson tumor control probability (TCP) model for sacral chordomas (SC) treated with carbon ion radiotherapy (CIRT), aiming to improve local control (LC) and local relapse (LR) prediction.</div></div><div><h3>Materials and Methods</h3><div>We considered data from 37 of the first 50 SC patients consecutively treated at the National Centre for Oncological Hadrontherapy (CNAO, Pavia, Italy). LQ Poisson formalism was revised to integrate either a linear (TCP_LIN) or logarithmic (TCP_LOG) dependence on clonogenic cell count, derived from baseline DWI through an optimal match with <em>in</em>-<em>silico</em> simulations. The models were compared with the case of a uniform cell density of 10⁷ cells/cm³, as widely adopted in the literature. All models were fitted on 27 patients and tested on 10 held-out cases to assess the performance, both in terms of area under the receiver-operator curve (AUC) and considering the statistical differences in TCP between LR and LC.</div></div><div><h3>Results</h3><div>In contrast to the constant cell density model, DWI-based models significantly separated the TCP of LC and LR patients, with TCP_LOG describing an average TCP of 71.3 % ± 9.56 % for LC patients, compared to 48.9 % ± 9.49 % for LR test cases. AUC values of 0.92 and 0.96 were respectively achieved by TCP_LIN and TCP_LOG, compared to 0.88 for constant cell density, on the test set.</div></div><div><h3>Conclusion</h3><div>DWI-based cell count data can significantly improve the performance of TCP models in predicting the probability of LC of SC treated with CIRT.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105038"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Deep Learning Models for fast and accurate dose map prediction in Microbeam Radiation Therapy","authors":"Lorenzo Arsini ,&nbsp;Jack Humphreys ,&nbsp;Christopher White ,&nbsp;Florian Mentzel ,&nbsp;Jason Paino ,&nbsp;David Bolst ,&nbsp;Barbara Caccia ,&nbsp;Matthew Cameron ,&nbsp;Andrea Ciardiello ,&nbsp;Stéphanie Corde ,&nbsp;Elette Engels ,&nbsp;Stefano Giagu ,&nbsp;Anatoly Rosenfeld ,&nbsp;Moeava Tehei ,&nbsp;Ah Chung Tsoi ,&nbsp;Sarah Vogel ,&nbsp;Michael Lerch ,&nbsp;Markus Hagenbuchner ,&nbsp;Susanna Guatelli ,&nbsp;Carlo Mancini Terracciano","doi":"10.1016/j.ejmp.2025.105012","DOIUrl":"10.1016/j.ejmp.2025.105012","url":null,"abstract":"<div><h3>Background and aim:</h3><div>Microbeam Radiation Therapy (MRT) is an innovative radiotherapy modality which uses highly focused synchrotron-generated X-ray microbeams. Current pre-clinical research in MRT mostly rely on Monte Carlo (MC) simulations for dose estimation, which are highly accurate but computationally intensive. Recently, Deep Learning (DL) dose engines have been proved effective in generating fast and reliable dose distributions in different RT modalities. However, relatively few studies compare different models on the same task. This work aims to compare a Graph-Convolutional-Network-based DL model, developed in the context of Very High Energy Electron RT, to the Convolutional 3D U-Net that we recently implemented for MRT dose predictions.</div></div><div><h3>Methods:</h3><div>The two DL solutions are trained with 3D dose maps, generated with the MC-Toolkit Geant4, in rats used in MRT pre-clinical research. The models are evaluated against Geant4 simulations, used as ground truth, and are assessed in terms of Mean Absolute Error, Mean Relative Error, and a voxel-wise version of the <span><math><mi>γ</mi></math></span>-index. Also presented are specific comparisons of predictions in relevant tumor regions, tissues boundaries and air pockets. The two models are finally compared from the perspective of the execution time and size.</div></div><div><h3>Results:</h3><div>This study finds that the two models achieve comparable overall performance. Main differences are found in their dosimetric accuracy within specific regions, such as air pockets, and their respective inference times. Consequently, the choice between models should be guided primarily by data structure and time constraints, favoring the graph-based method for its flexibility or the 3D U-Net for its faster execution.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105012"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is the use of machine learning in head and neck cancer radiotherapy supported by clinical trials?","authors":"Ioana-Claudia Costin ,&nbsp;Loredana G. Marcu ,&nbsp;David C. Marcu ,&nbsp;Renata Zahu ,&nbsp;Oreste Straciuc","doi":"10.1016/j.ejmp.2025.105036","DOIUrl":"10.1016/j.ejmp.2025.105036","url":null,"abstract":"<div><h3>Objective</h3><div>The employment of artificial intelligence (AI) and machine learning (ML) in cancer management is well documented. The diversity and complexity of models, approaches, endpoints and anatomies makes it difficult to draw pertinent conclusions regarding the usefulness of ML in various applications. Given that clinical trials, particularly randomized ones, are the gold standard when testing new techniques, the current work aims to collate trials that investigated the effectiveness of ML in head and neck cancer (HNC).</div></div><div><h3>Methods</h3><div>A systematic search of Medline/PubMed and Web of Science databases was performed to identify clinical trials reporting the use of AI / ML in HNC for either of the following clinical aspects: detection / classification, image segmentation and treatment response / dose distribution prediction. Of the 2395 identified studies, 42 met the eligibility criteria.</div></div><div><h3>Results</h3><div>Most studies confirmed the usefulness of ML in HNC clinical applications through reported parameters: accuracy, area under the curve, specificity, sensitivity, dice coefficient. Deep learning models utilizing multiple layered neural networks were often the choice as they outperform machine learning in predictive model development. However, traditional algorithms (KNN, SVM, logistic regression) often showed comparable results with deep learning. The most commonly used radiomic features were related to image texture irrespective of the model’s application.</div></div><div><h3>Conclusions</h3><div>Clinical trial results show a supportive role for ML in HNC management, whether diagnostic/staging- or treatment-related. The best performing applications are enhancing deep learning algorithms applied to imaging with clinical data for a more comprehensive and better-integrated approach to personalized treatment.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105036"},"PeriodicalIF":3.3,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of iterative vs AI-based reconstruction algorithms in CT imaging for total body assessment: Objective and subjective clinical analysis","authors":"Raffaele Maria Tucciariello ,&nbsp;Manuela Botte ,&nbsp;Giovanni Calice ,&nbsp;Aldo Cammarota ,&nbsp;Flavia Cammarota ,&nbsp;Mariagrazia Capasso ,&nbsp;Giuseppina Di Nardo ,&nbsp;Maria Imma Lancellotti ,&nbsp;Valentina Pirozzi Palmese ,&nbsp;Antonio Sarno ,&nbsp;Antonio Villonio ,&nbsp;Antonella Bianculli","doi":"10.1016/j.ejmp.2025.105034","DOIUrl":"10.1016/j.ejmp.2025.105034","url":null,"abstract":"<div><h3>Purpose</h3><div>This study evaluates the performance of Iterative and AI-based Reconstruction algorithms in CT imaging for brain, chest, and upper abdomen assessments. Using a 320-slice CT scanner, phantom images were analysed through quantitative metrics such as Noise, Contrast-to-Noise-Ratio and Target Transfer Function. Additionally, five radiologists performed subjective evaluations on real patient images by scoring clinical parameters related to anatomical structures across the three body sites.</div></div><div><h3>Methods</h3><div>The study aimed to relate results obtained with the typical approach related to parameters involved in medical physics using a Catphan physical phantom, with the evaluations assigned by the radiologists to the clinical parameters chosen in this study, and to determine whether the physical approach alone can ensure the implementation of new procedures and the optimization in clinical practice.</div></div><div><h3>Results</h3><div>AI-based algorithms demonstrated superior performance in chest and abdominal imaging, enhancing parenchymal and vascular detail with notable reductions in noise. However, their performance in brain imaging was less effective, as the aggressive noise reduction led to excessive smoothing, which affected diagnostic interpretability. Iterative reconstruction methods provided balanced results for brain imaging, preserving structural details and maintaining diagnostic clarity.</div></div><div><h3>Conclusions</h3><div>The findings emphasize the need for region-specific optimization of reconstruction protocols. While AI-based methods can complement traditional IR techniques, they should not be assumed to inherently improve outcomes. A critical and cautious introduction of AI-based techniques is essential, ensuring radiologists adapt effectively without compromising diagnostic accuracy.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105034"},"PeriodicalIF":3.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a versatile deep hyperthermia treatment planning tool","authors":"Tomas Drizdal ,&nbsp;Margarethus M. Paulides ,&nbsp;Daniel de Jong ,&nbsp;Marek Novak ,&nbsp;Tomas Pokorny ,&nbsp;Zdenek Linha ,&nbsp;Jakub Kollar ,&nbsp;Martine Franckena ,&nbsp;Ondrej Fiser ,&nbsp;Sergio Curto ,&nbsp;Gerard C. van Rhoon","doi":"10.1016/j.ejmp.2025.105035","DOIUrl":"10.1016/j.ejmp.2025.105035","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and test a versatile deep hyperthermia treatment planning (HTP) tool.</div></div><div><h3>Methods</h3><div>A patient automatic positioning routine following manual clinical procedure was implemented into a newly developed versatile HTP tool. For 70 patients treated using Sigma 60 applicator, 48 patients with the Sigma Eye applicator, we compared predicted specific absorption rate (SAR) indicators obtained by manual and automatic positioning routines. We used the target to hotspot coefficient (THQ) representing the ratio between average SAR in target and hotspot, TC25 and TC50 defined as target volume enclosed with 25% and 50% SAR iso-contour. Further, we created example HTP setups for Sigma Ellipse, Alba4D and AMC8 applicators demonstrating the versatility of the tool.</div></div><div><h3>Results</h3><div>For the Sigma 60 and the Sigma Eye, we found a highest average relative difference of 2% for all studied SAR indicators when comparing manual and automatically created HTP setups. Overall, the automatic positioning procedure is highly efficient and operator time for building a single patient specific HTP setup is 10–20 min faster.</div></div><div><h3>Conclusion</h3><div>On average, the automatic positioning routine is more time efficient and provides similar SAR target coverage compared to manual positioning by experienced staff. The reduced time requirement supports wider HTP acceptance in the clinical routine.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 105035"},"PeriodicalIF":3.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing high-quality enhanced 4D-MRI with personalized modeling for liver cancer radiotherapy","authors":"Yuhe Yao ,&nbsp;Bo Chen ,&nbsp;Ke Wang ,&nbsp;Ying Cao ,&nbsp;Lijing Zuo ,&nbsp;Kaixuan Zhang ,&nbsp;Xinyuan Chen ,&nbsp;Men Kuo ,&nbsp;Jianrong Dai","doi":"10.1016/j.ejmp.2025.104955","DOIUrl":"10.1016/j.ejmp.2025.104955","url":null,"abstract":"<div><h3>Background</h3><div>For magnetic resonance imaging (MRI), a short acquisition time and good image quality are incompatible. Thus, reconstructing time-resolved volumetric MRI (4D-MRI) to delineate and monitor thoracic and upper abdominal tumor movements is a challenge. Existing MRI sequences have limited applicability to 4D-MRI.</div></div><div><h3>Purpose</h3><div>A method is proposed for reconstructing high-quality personalized enhanced 4D-MR images. Low-quality 4D-MR images are scanned followed by deep learning–based personalization to generate high-quality 4D-MR images.</div></div><div><h3>Methods</h3><div>High-speed multiphase 3D fast spoiled gradient recalled echo (FSPGR) sequences were utilized to generate low-quality enhanced free-breathing 4D-MR images and paired low-/high-quality breath-holding 4D-MR images for 58 liver cancer patients. Then, a personalized model guided by the paired breath-holding 4D-MR images was developed for each patient to cope with patient heterogeneity.</div></div><div><h3>Results</h3><div>The 4D-MR images generated by the personalized model were of much higher quality compared with the low-quality 4D-MRI images obtained by conventional scanning as demonstrated by significant improvements in the peak signal-to-noise ratio, structural similarity, normalized root mean square error, and cumulative probability of blur detection. The introduction of individualized information helped the personalized model demonstrate a statistically significant improvement compared to the general model (p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>The proposed method can be used to quickly reconstruct high-quality 4D-MR images and is potentially applicable to radiotherapy for liver cancer.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"136 ","pages":"Article 104955"},"PeriodicalIF":3.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}