An MRI-only adaptive radiotherapy (ART) workflow is desirable for managing interfractional changes in anatomy, but producing synthetic CT (sCT) data through paired data-driven deep learning (DL) for abdominal dose calculations remains a challenge due to the highly variable presence of intestinal gas. We present the preliminary dosimetric evaluation of our novel approach to sCT reconstruction that is well suited to handling intestinal gas in abdominal MRI-only ART.
We utilize a paired data DL approach enabled by the intensity projection prior, in which well-matching training pairs are created by propagating air from MRI to corresponding CT scans. Evaluations focus on two classes: patients with (1) little involvement of intestinal gas, and (2) notable differences in intestinal gas presence between corresponding scans. Comparisons between sCT-based plans and CT-based clinical plans for both classes are made at the first treatment fraction to highlight the dosimetric impact of the variable presence of intestinal gas.
Class 1 patients ($n=13$) demonstrate differences in prescribed dose coverage of the PTV of $1.3 \pm 2.1\%$ between clinical plans and sCT-based plans. Mean DVH differences in all structures for Class 1 patients are found to be statistically insignificant. In Class 2 ($n=20$), target coverage is $13.3 \pm 11.0\%$ higher in the clinical plans and mean DVH differences are found to be statistically significant.
Significant deviations in calculated doses arising from the variable presence of intestinal gas in corresponding CT and MRI scans may limit the effectiveness of adaptive dose escalation efforts. We have proposed a paired data-driven DL approach to sCT reconstruction for accurate dose calculations in abdominal ART enabled by the creation of a clinically unavailable training data set with well-matching representations of intestinal gas.
Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy. A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n=8) and non-liver abdominal (n=4) cancer. CT images were deformably registered to the c…
Purpose: Bowel motion is a significant source of artifacts in mouse abdominal MRI. Fasting and administration of hyoscine butylbromide (BUSC) have been proposed for bowel motion reduction, but with inconsistent results and limited efficacy assessments. Here, we evaluate these regimes for mouse abdominal MRI at high field. Methods: Thirty-two adult C57BL/6J mice were imaged on a 9.4T scanner with a FLASH sequence, acquired over 90 minutes with ~19s temporal resolution. During MRI acquisition, eight mice were injected with a low-dose and eight mice with a high-dose bolus of BUSC (0.5 and 5 mg/kg…
Purpose: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep learning approach. Material and Methods: We conducted a study in 77 patients with brain tumors who had undergone both MRI and computed tomography (CT) imaging as part of their simulation for external beam treatment planning. We designed a generative adversarial network (GAN) to generate synthetic CT images from MRI images. We used Mutual Information (MI) as the loss function in the generator to overcome the misalignment…
Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Princess Màxima Center for Pediatric Oncology（玛克西玛公主儿科肿瘤中心）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）
To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs ($n$=142) of rec…
MRI-guided cardiac radiotherapy using a 1.5 T MR-linac: surveying emerging patterns of care
University Medical Center Utrecht（乌得勒支大学医学中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）；Allegheny Health Network（阿勒格尼健康网络）；GenesisCare（创生医疗）
Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
Princess Máxima Centre for Pediatric Oncology；University Medical Centre Utrecht；Centre for Image Sciences, University Medical Centre Utrecht；University Hospital Heidelberg；University Medical Center Utrecht；Division Imaging & Cancer, University Medical Center Utrecht；Division of Pediatric Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg；Wilhelmina Children’s Hospital-Division of CHILD HEALTH, University Medical Centre Utrecht, University of Utrecht
Automatic quantification of abdominal subcutaneous and visceral adipose tissue in children, through MRI study, using total intensity maps and Convolutional Neural Networks
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Sichuan Cancer Hospital & Institute（四川省肿瘤医院研究所）；University of Electronic Science and Technology of China（电子科技大学）；LMU University Hospital（LMU大学医院）；LMU Munich（慕尼黑大学）；Catharina Hospital（凯瑟琳医院）；Mater Olbia Hospital（奥尔比亚 Mater 医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Czech Technical University in Prague（布拉格捷克理工大学）；University of Sydney（悉尼大学）；Fondazione Policlinico Universitario Agostino Gemelli, IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会 IRCCS）；Abano Terme Hospital（阿巴诺泰尔梅医院）；GenesisCare；St Vincent’s Hospital（圣文森特医院）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹自由大学医学中心）；Technical University Eindhoven（埃因霍温理工大学）
Dose painting has been developed to modulate the required dose in the target area without increasing the toxicity in healthy areas. Apart from determining the accurate location and size of tumors, quantitative functional imaging can be used to implement the dose painting. Functional imaging, such as multi-parameter MRI and PET CT, allows us to achieve biological dose escalation by increasing the dose in certain areas or voxels that are therapy-resistant in the gross tumor volume while reducing the dose in the less aggressive area or voxels. Functional imaging can serve as an indicator of thera…
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Millenium Institute for Intelligent Healthcare Engineering（千禧智能医疗工程研究所）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）
Purpose: Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed metho…
Hybrid MRI-linac (MRL) systems enable daily multiparametric quantitative MRI to assess tumor response to radiotherapy. Magnetic Resonance Fingerprinting (MRF) may provide time efficient means of rapid multiparametric quantitative MRI. The accuracy of MRF, however, relies on adequate control over system imperfections, such as eddy currents and B1+, which are different and not as well established on MRL systems compared to diagnostic systems. In this study we investigate the technical feasibility of gradient spoiled 2D MRF on a 1.5T MRL. We show with phantom experiments that the MRL generates re…
The Role of Free-breathing GRASP MRI in Accurate Phase Matching with 4D-CT for Motion Representation in Liver Cancer Radiotherapy
Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology（华中科技大学同济医学院同济医院）；Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院/国家癌症中心/国家临床医学研究中心放射肿瘤科）；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）
Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning
Physics and Imaging in Radiation Oncology (phiRO)（物理与放射治疗成像（phiRO））
A Novel Automatic Real-time Motion Tracking Method in MRI-guided Radiotherapy Using Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（国家癌症中心/国家癌症临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院）
Purpose: To assess the fiducial motion in abdominal stereotactic body radiotherapy (SBRT) using the cone-beam computed tomography (CBCT) projections acquired for pre-treatment patient set-up. Materials and Methods: Pre-treatment CBCT projections and anterior-posterior (AP) and lateral (LAT) pair of fluoroscopic sequences of 7 pancreatic and 6 liver SBRT patients with implanted fiducials were analyzed for 49 treatment fractions retrospectively. A tracking algorithm based on template matching and sequential stereo triangulation algorithms was used to track the fiducials in the CBCT projections a…
Magnetic resonance imaging assessment of the suitability and consistency of radiotherapy treatment positioning achieved using intra-oral stents
Royal Brisbane and Women's Hospital（皇家布里斯班和妇女医院）；University of Queensland（昆士兰大学）；Queensland University of Technology（昆士兰科技大学）；Herston Biofabrication Institute, Metro North Hospital and Health Service（赫斯顿生物制造研究所，北都会区医院与健康服务）
Intra-frame motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly-accelerated 4D (3D+time) real-time MRI framework with sub-second temporal resolution combining standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel) reconstruction. Specifically, 4D real-time MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide est…
Magnetic resonance imaging (MRI) is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearity (GNL) limit anatomical accuracy, potentially compromising the quality of tumour treatments. In addition, slow MR acquisition and reconstruction limit the potential for real-time image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for real-time MR-guided radiotherapy applications. We…
Intrafraction motion can play a pivotal role in the success of abdominal and thoracic radiation therapy. Hybrid magnetic resonance-guided radiotherapy systems have the potential to control for intrafraction motion. Recently, we introduced an MRI sequence capable of acquiring real-time cine imaging in two orthogonal planes (SOPI). We extend SOPI here to permit dynamic updating of slice positions in one plane while keeping the other plane position fixed. In this implementation, cine images from the static plane are used for motion monitoring and as image navigators to sort stepped images in the …
DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany（路德维希-马克西米利安慕尼黑大学医学院放射肿瘤科，慕尼黑大学医院，慕尼黑，德国）；Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany（放射肿瘤学中的医学物理部门，德国癌症研究中心（DKFZ），海德堡，德国）；National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany（肿瘤放射研究国家中心（NCRO），海德堡放射肿瘤学研究所（HIRO），海德堡，德国）；GSI Helmholtzzentrum für Schwerionenforschung, Biophysics Department, Darmstadt, Germany（重离子研究中心GSI，生物物理部门，达姆施塔特，德国）；Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany（海德堡大学物理与天文学系，海德堡，德国）；Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Baden-Württemberg, Germany（生物医学工程研究所，卡尔斯鲁厄理工学院，巴登-符腾堡，德国）；Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands（放射治疗科，乌得勒支大学医学中心，乌得勒支，荷兰）；Department of Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands（放射肿瘤学系，阿姆斯特丹自由大学阿姆斯特丹校区，阿姆斯特丹癌症中心，癌症治疗与生活质量，阿姆斯特丹，荷兰）；Center for Proton Therapy, Paul Scherrer Institute (PSI), Villigen, Switzerland（质子治疗中心，瑞士保罗·谢尔研究所（PSI），维利根，瑞士）；Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany（放射肿瘤学系，海德堡大学医院，海德堡，德国）；Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden（放射物理，血液学、肿瘤学和放射物理部门，斯德哥尔摩大学医院，吕勒奥，瑞典）；Delft University of Technology, Department of Radiation Science and Technology, Delft, Netherlands（代尔夫特理工大学，辐射科学与技术系，代尔夫特，荷兰）；Radformation Inc., New York, US（Radformation公司，纽约，美国）
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床医学研究中心/肿瘤医院）
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic-CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images to be used for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate, rectal and cervical cancer who underwent external beam radiotherapy acquir…
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心，格罗宁根大学）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy
Faculty of Medicine and Health, The University of Sydney（澳大利亚悉尼大学医学与健康学院）；Ingham Institute for Applied Medical Research（英厄姆应用医学研究所）；A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院A.A.马蒂诺斯生物医学成像中心）
Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data
Medical Center University of Freiburg（弗莱堡大学医学中心）；University Medical Center Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医学中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；German Cancer Consortium (DKTK)（德国癌症联盟）
SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy
LMU University Hospital, LMU Munich（慕尼黑大学附属医院，慕尼黑大学）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心，格罗宁根大学）；University Hospital of Cologne（科隆大学附属医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Skåne University Hospital（斯科讷大学医院）；Lund University（隆德大学）；Deutsches Krebsforschungszentrum (DKFZ)（德国癌症研究中心）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Mate…
Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
GE Research（通用电气研究院）；GE Healthcare（通用电气医疗）；Mayo Clinic（妙佑医疗国际）；Walter Reed National Military Medical Center（沃尔特里德国家军事医疗中心）；Uniformed Services University（美国三军卫生科学大学）
Purpose: To accelerate brain 3D MRI scans by using a deep learning method for reconstructing images from highly-undersampled multi-coil k-space data Methods: DL-Speed, an unrolled optimization architecture with dense skip-layer connections, was trained on 3D T1-weighted brain scan data to reconstruct complex-valued images from highly-undersampled k-space data. The trained model was evaluated on 3D MPRAGE brain scan data retrospectively-undersampled with a 10-fold acceleration, compared to a conventional parallel imaging method with a 2-fold acceleration. Scores of SNR, artifacts, gray/white ma…
Purpose: Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative MRI image analysis in daily clinical practice. In this study, we present a deep learning-based approach for MRI image INU correction. Method: We developed a residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected MRI images to constrain the model through forcing …
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
Radiation Physics, Department of Hematology, Oncology；Radiation Physics, Skåne University Hospital, Lund, Sweden；Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden；Radiotherapy Department, University Medical Center Utrecht, Utrecht, The Netherlands；Computational Imaging Group for MR Diagnostics \& Therapy, University Medical Center Utrecht, Utrecht, The Netherlands；Division of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany；Heidelberg Institute for Radiation Oncology (HIRO)；National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany；Department of Radiation Oncology；Cyberknife Center, University Hospital of Cologne, Cologne, Germany；Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands；Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands；Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany；Bavarian Cancer Research Center (BZKF), Munich, Germany；Australian eHealth Research Center, CSIRO, Brisbane, Australia；School of Computer Science, University of Sydney, Sydney, Australia；Department of Orthodontics；Pattern Recognition Lab, FAU Erlangen-Nuremberg, Germany；RIKEN Center for Integrative Medical Sciences, Tokyo, Japan；Erasmus Mundus Joint Master's Degree IPCVai, University of Bordeaux, France；Computer Science, Huazhong University of Science；Huazhong University of Science；Canon Medical Systems (China) CO., LTD., Beijing, China；Department of Radiation Oncology, Inselspital, Bern University Hospital；University of Bern, Bern, Switzerland
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Human Technopole（人类技术园）；German Centre for Neurodegenerative Disease（德国神经退行性疾病中心）；Centre for Behavioural Brain Sciences（行为脑科学中心）
Computed tomography and magnetic resonance imaging are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalit…
