Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning
National Taiwan University of Science and Technology（台湾科技大学）；National Taiwan University Hospital（台湾大学医院）；National Taiwan University（台湾大学）
Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao, Hsing-Kuo Pao
备注：
This preprint has been accepted for publication in the proceedings of the IEEE 23rd International Symposium on Biomedical Imaging (ISBI 2026). The final published version is available at https://doi.org/10.1109/ISBI61048.2026.11515734. The copyright for this work has been transferred to IEEE
发表信息：
Proceedings of the 23rd IEEE International Symposium on Biomedical Imaging (ISBI), 2026
MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation. As a result, current radiotherapy workflows rely on combined MRI and CT acquisitions, increasing registration uncertainty and procedural complexity. Synthetic CT generation enables MRI only planning but remains challenging due to nonlinear MRI-CT relationships and anatomical variability. We propose Parallel Swin Transformer-Enhanced Med2Transformer, a 3D architecture that integrates convolutional encoding with dual Swin Transformer branches to model both local anatomical detail and long-range contextual dependencies. Multi-scale shifted window attention with hierarchical feature aggregation improves anatomical fidelity. Experiments on public and clinical datasets demonstrate higher image similarity and improved geometric accuracy compared with baseline methods. Dosimetric evaluation shows clinically acceptable performance, with a mean target dose error of 1.69%. Code is available at: https://github.com/mobaidoctor/med2transformer.
Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning
School of Electrical；Computer Engineering, National Technical University of Athens Archimedes, Athena Research Center CentraleSup\'elec, University of Paris-Saclay
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）
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）；School of Biomedical Engineering, Sun Yat-sen University（中山大学生物医学工程学院）
Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based models, are fast-growing in natural language processing and promptly developed for computer vision and medical image analysis due to their prominent performance. Nevertheless, due to the complexity of the Transformer, the application of fast MRI may not be straightforward. The main obstacle is the computational cost of the self-attention layer, which is the co…
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）
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Sun Yat-sen University（中山大学）；Beihang University（北京航空航天大学）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；TECNALIA（泰克纳利亚研究中心）；Shenzhen Second People’s Hospital（深圳市第二人民医院）；The First Affiliated Hospital of Shenzhen University Health Science Center（深圳大学医学部第一附属医院）
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiological movements. To accelerate the scanning process, methods by k-space undersampling and deep learning based reconstruction have been popularised. This work introduced SwinMR, a novel Swin transformer based method for fast MRI reconstruction. The who…
Monash Biomedical Imaging, Monash University（莫纳什大学生物医学成像中心）；Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）；School of Psychological Sciences, Monash University（莫纳什大学心理科学学院）；Department of Data Science and AI, Monash University（莫纳什大学数据科学与人工智能系）
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI) scans as a result of close-range proton density and T1/T2 parameters. Furthermore, to achieve accelerated MRI, k-space data are undersampled which causes global aliasing artifacts. Convolutional neural network (CNN) models are widely utilized for accelerated MRI reconstruction, but those models are limited in capturing global correlations due to the intrinsic locality of the convolution operation. The self-attention-…
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
Mask-Guided Multi-Channel SwinUNETR Framework for Robust MRI Classification
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（巴塞罗那大学数学与计算机系）；Computer Vision Center, Bellaterra, Spain（贝尔特拉拉计算机视觉中心）；Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain（加泰罗尼亚高级研究与教育研究所）
Background and objective: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors are critical. However, the relative rarity of these tumors and the highly dispersed tissue types have left an unmet need for a subtle differential diagnosis of such neoplastic lesions based on preoperative radiomics. Recently, deep learning methods have developed rapidly, especially Transformer beats the traditional convolutional neural network in computer v…
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）
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Centre Antoine Lacassagne（安托万·拉卡桑中心）
Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and non-exclusive class…
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Universitätsklinikum Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲分子神经科学网络综合癌症中心）
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）
Yale University（耶鲁大学）；New York University（纽约大学）；Hyperfine Research（Hyperfine研究公司）
Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities to aid in radiological decision-making. Given the need for lowering the time cost of multiple acquisitions, current deep accelerated MRI reconstruction networks focus on exploiting the redundancy between multiple contrasts. However, existing works are largely supervised with paired data and/or prohibitively expensive fully-sampled MRI sequences. Further, reconstruction networks typically rely on convolutional architectures which are limited in their capacity to model long-range interactions and may lead to suboptima…
SST-DUNet: Automated preclinical functional MRI skull stripping using Smart Swin Transformer and Dense UNet
School of Information Technology, Carleton University（卡尔顿大学信息技术学院）；Center for Translational NeuroImaging (CTNI), Northeastern University（东北大学转化神经影像学中心）；College of Science and Humanity Studies, Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学科学与人文研究学院）；Tessellis Ltd.（泰塞利斯有限公司）
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）
Université de Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；CGFL（CGFL（第戎乔治·弗朗索瓦·勒克莱尔中心））；University Hospital of Dijon（第戎大学医院）
Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven m…
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）
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）
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Radiology and Imaging Sciences（放射学与影像科学）；National Institutes of Health Clinical Center（国家卫生研究院临床中心）；Walter Reed National Military Medical Center（沃尔特·里德国家军事医学中心）；Biostatistics and Clinical Epidemiology Services（生物统计与临床流行病学服务）
Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI
Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia（悉尼大学医学与健康学院悉尼健康科学学院Image X研究所，悉尼，澳大利亚）；GenesisCare, Sydney, Australia（GenesisCare，悉尼，澳大利亚）；School of Clinical Medicine, Medicine & Health, University of New South Wales, Sydney, Australia（新南威尔士大学医学与健康学院临床医学系，悉尼，澳大利亚）；Centre for Medical Radiation Physics, School of Mathematics and Physics, University of Wollongong, Wollongong, Australia（伍伦贡大学数学与物理学院医学辐射物理中心，伍伦贡，澳大利亚）
Shanghai Jiao Tong University（上海交通大学）；Shandong Cancer Hospital Affiliated to Shandong University（山东大学附属山东省肿瘤医院）；Deepwise AI Lab（深睿医疗人工智能实验室）
Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to captu…
In this work, we address the TrackRAD2025 challenge of real-time tumor tracking in cine-MRI sequences of the thoracic and abdominal regions under strong data scarcity constraints. Two complementary strategies were explored: (i) unsupervised registration with the IMPACT similarity metric and (ii) foundation model-based segmentation leveraging SAM 2.1 and its recent variants through prompt-based interaction. Due to the one-second runtime constraint, the SAM-based method was ultimately selected. The final configuration used SAM2.1 b+ with mask-based prompts from the first annotated slice, fine-tu…
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
Institute of Biomedical Engineering（生物医学工程研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学）；Department of Radiation Oncology（放射肿瘤科）；University Medical Center Schleswig-Holstein（什勒斯威-霍斯特大学医院）；Department of Experimental and Clinical Medicine（实验与临床医学系）；Magna Graecia University（马格拉西亚大学）；Department of Information Engineering（信息工程系）；Università Politecnica delle Marche（帕尔米拉理工大学）
KTH Royal Institute of Technology（KTH皇家理工学院）；Science for Life Laboratory（生命科学实验室）
Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render a scan useless, but can potentially induce misdiagnoses if left unnoticed. For instance, an artifact may masquerade as a tumor or other abnormality. Retrospective artifact correction (RAC) is concerned with removing artifacts after the scan has already been taken. In this work, …
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-superv…
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）
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）
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Accurate and robust segmentation of lung cancers from CT, even those located close to mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure treatment response. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby an informative teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background. Our contribution eliminates two requirements of distillation methods: (i) paired image sets by using an image to image (I2I) translation a…
NeuroVascU-Net: A Unified Multi-Scale and Cross-Domain Adaptive Feature Fusion U-Net for Precise 3D Segmentation of Brain Vessels in Contrast-Enhanced T1 MRI
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（电信工程学院，工程学院，德黑兰大学）；Department of EECS, Lassonde School of Engineering, York University（电子工程系，拉索nde工程学院，约克大学）；Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences（神经外科系，医学院，伊朗医学科学大学）；Department of Electrical and Computer Engineering, University of Waterloo（电子工程系，滑铁库大学）
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院·研究所）
Radiotherapy is the main treatment modality for nasopharynx cancer. Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy. As manual delineation is time-consuming and laborious, automatic segmentation of GTV has a potential to improve this process. Currently, most of the deep learning-based automatic delineation methods of GTV are mainly performed on medical images like CT images. However, it is challenged by the low contrast between the pathology regions and surrounding soft tissues, small target region, and anisotropic resol…
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）
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method for clinical interrogation of organ structures and metabolism, with an inherently long scanning time. Methods by k-space undersampling and deep learning based reconstruction have been popularised to accelerate the scanning process. This work focuses on investigating how powerful transformers are for fast MRI by exploiting and comparing different novel network architectures. In particular, a generative adversarial network (GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI reconstruction. T…
Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited…
Three-dimensional end-to-end deep learning for brain MRI analysis
Technical University Dresden（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院及医学院）；National Center for Tumor Diseases Dresden（德累斯顿国家肿瘤疾病中心）；University Hospital Heidelberg（海德堡大学医院）；National Center for Tumor Diseases（国家肿瘤疾病中心）；University Hospital Aachen（亚琛大学医院）；Carl Gustav Carus University Hospital, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院）
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
Bioengineering Department（生物工程系）；Imperial-X, Imperial College London（帝国理工学院Imperial-X）；Imperial College London（帝国理工学院）；National Heart and Lung Institute, Imperial College London（国家心脏和肺 institute, 帝国理工学院）；Cardiovascular Research Centre, Royal Brompton Hospital（心血管研究中心, 罗伊·布罗姆普顿医院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与成像科学学院, 伦敦国王学院）
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.（南方医科大学生物医学工程学院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging（广东省医学影像与诊断技术工程实验室）；Information Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.（南方医科大学南华医院信息中心）；Division of Spine Surgery, Department of Orthopaedics, Nanfang hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.（南方医科大学南华医院骨科脊柱外科）
