Using constraint structure and an improved object detection network to detect the 12^{th} Vertebra from CT images with a limited field of view for image-guided radiotherapy
Massachusetts General Hospital（麻省总医院）；CVCS
Yunhe Xie, Kongbin Kang, Gregory Sharp, David P. Gierga, Theodore S. Hong, Thomas Bortfeld
Image guidance has been widely used in radiation therapy. Correctly identifying the bounding box of the anatomical landmarks from limited field of views is the key to success. In image-guided radiation therapy (IGRT), the detection of those landmarks like the 12th vertebra (T12) still requires tedious manual inspections and annotations; and superior-inferior misalignment to the wrong vertebral body is still relatively common. It is necessary to develop an automated approach to detect those landmarks from images. The challenges of training a model to identify T12 vertebrae automatically mainly are high shape similarity between T12 and neighboring vertebrae, limited annotated data, and class imbalance. This study proposed a novel 3D detection network, requiring only a small amount of training data. Our approach has the following innovations, including 1) the introduction of an auxiliary network to build constraint feature map for improving the model's generalization, especially when the constraint structure is easier to be detected than the main one; 2) an improved detection head and target functions for accurate bounding box detection; and 3) an improved loss functions to address the high class imbalance. Our proposed network was trained, validated and tested on anotated CT images from 55 patients and demonstrated accurate distinguish T12 vertebra from its neighboring vertebrae of high shape similarity. Our proposed algorithm yielded the bounding box center and size errors of 3.98\pm2.04mm and 16.83\pm8.34mm, respectively. Our approach significantly outperformed state-of-the-arts Retina-Net3D in average precision (AP) at IoU thresholds of 0.35 and 0.5, with AP increasing from 0 to 95.4 and 0 to 64.7, respectively. In summary, our approach has a great potential to be integrated into the clinical workflow to improve the safety of IGRT.
Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
School of Computer Science, Sichuan University（四川大学计算机学院）；Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University（四川大学华西医院肿瘤中心放射肿瘤科）
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）
Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southern University of Science and Technology（南方科技大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）
An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer
Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099（雷恩大学，尤金·马奎斯中心，法国国家健康与医学研究院，LTSI - UMR 1099）；Department of Radiation Oncology, Centre Georges-Francois Leclerc（乔治-弗朗索瓦·勒克莱尔中心放射肿瘤科）；Institut de Cancérologie de l’Ouest–Site Paul Papin（西部癌症研究所-保罗·帕潘院区）；CLCC Henri Becquerel（亨利·贝克勒尔中心）
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）
Development of an Unpaired Deep Neural Network for Synthesizing X-ray Fluoroscopic Images from Digitally Reconstructed Tomography in Image Guided Radiotherapy
Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology（量子科学技术研究开发机构量子医疗科学研究所）；Graduate School of Science and Engineering, Chiba University（千叶大学大学院理工学研究科）；Graduate School of Engineering, Chiba University（千叶大学大学院工学研究科）；QST hospital, National Institutes for Quantum Science and Technology（量子科学技术研究开发机构QST医院）
ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR（香港理工大学健康科技与信息技术系）；The Hong Kong Polytechnic University Shenzhen Research Institute, The Hong Kong Polytechnic University, China（香港理工大学深圳研究院）；Department of Orthodontics and Orofacial Orthopedics, Friedrich-Alexander-University Erlangen-Nuremberg, Germany（弗赖堡-埃尔兰根-纽伦堡大学口腔医学与面部骨科系）；Institute of Scientific Instrumentation, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China（深圳先进技术研究所科学仪器研究所）；Department of Computer and Information Science, University of Macau, Macau SAR（澳门大学计算机与信息科学系）
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Duke University Medical Center（杜克大学医学中心）
DINOMotion: advanced robust tissue motion tracking with DINOv2 in 2D-Cine MRI-guided radiotherapy
Department of Computer Science and Software Engineering, Concordia University（计算机科学与软件工程系，康科迪亚大学）；Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）；Elekta Ltd.（Elekta公司）；Diagnos Medical Systems（Diagnos医疗系统）
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）
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）
Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss
University of California, Los Angeles（加州大学洛杉矶分校）；Tencent Hippocrates Research Lab（腾讯希波克拉底研究实验室）；The University of Hong Kong（香港大学）；VoxelCloud, Inc.（VoxelCloud公司）
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）
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
Sharif University of Technology（谢里夫理工大学）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学中心人工智能医学研究所）；Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心/LASI）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR)（埃森大学医学中心（公法团体）科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；TU Dortmund University（多特蒙德工业大学）
SpinalSAM-R1: A Vision-Language Multimodal Interactive System for Spine CT Segmentation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）；Department of Orthopedics, Qilu Hospital, Shandong University（骨科部，齐鲁医院，山东大学）；Key Laboratory of Qingdao in Medicine and Engineering（医学与工程青岛重点实验室）
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）
End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
Samsung Research（三星研究院）；Center for Advanced Medical Computing and Analysis (CAMCA)（先进医学计算与分析中心）；Massachusetts General Hospital (MGH) and Harvard Medical School（麻省总医院和哈佛医学院）；Yonsei University College of Medicine（延世大学医学院）；Yonsei University（延世大学）；Institute for Innovation in Digital Healthcare（数字医疗创新研究所）；Yongin Severance Hospital（Yongin Severance医院）；Gachon University Gil Hospital（高仁大学Gil医院）；Oncosoft Inc（Oncosoft公司）；Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)（人工智能研究生院，韩国科学技术院）
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Peking University Third Hospital（北京大学第三医院）；Beijing Children’s Hospital, Capital Medical University（首都医科大学附属北京儿童医院）；Duke University（杜克大学）
DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT
Faculty of Information Technology, Monash University（墨尔本大学信息科技学院）；Institute of Artificial Intelligence, School of Computer Science, Wuhan University（武汉大学计算机科学学院人工智能研究所）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
Department of Electrical \& Computer Engineering, Rice University, Houston TX, USA Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston TX, USA
Topology-Guided Biomechanical Profiling: A White-Box Framework for Opportunistic Screening of Spinal Instability on Routine CT
Southern Medical University（南方医科大学）；The Affiliated Cancer Hospital of Zhengzhou University（郑州大学附属肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）；Xi'an Jiaotong University（西安交通大学）；Northwestern Polytechnical University（西北工业大学）；The University of Hong Kong（香港大学）；Westlake University（西湖大学）；Guangdong Provincial People's Hospital（广东省人民医院）
PGR-Net: Prior-Guided ROI Reasoning Network for Brain Tumor MRI Segmentation
College of Information Engineering, Capital Normal University（首都师范大学信息工程学院）；School of Artificial Intelligence, China University of Mining and Technology-Beijing（中国矿业大学(北京)人工智能学院）
Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention
University of Electronic Science and Technology of China（电子科技大学）；University of Maryland School of Medicine（马里兰大学医学院）；Rutgers, the State University of New Jersey（罗格斯大学（新泽西州立大学））
A Proof-of-Concept Study of Multitask Learning for Cranial Synthetic CT Generation Across Heterogeneous MRI Field Strengths
F.M. Kirby Research Center for Brain Imaging, Kennedy Krieger Institute（F.M. Kirby脑成像研究中心，Kennedy Krieger研究所）；Neurosection, Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine（神经科，磁共振研究部，约翰·霍普金斯大学医学院放射学与放射科学系）；Department of Biomedical Engineering, Johns Hopkins University（生物医学工程系，约翰·霍普金斯大学）；Department of Biomedical Engineering, Case Western Reserve University（生物医学工程系，凯斯西储大学）；Department of Biomedical Engineering, Columbia University（生物医学工程系，哥伦比亚大学）；Department of Neuroscience, Columbia University（神经科学系，哥伦比亚大学）；Department of Radiology, Mayo Clinic（放射科，梅奥诊所）；Neuroradiology and Neurosurgery, Mayo Clinic College of Medicine and Science（神经放射学与神经外科，梅奥诊所医学院与科学学院）
