Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction. However, the current DM-based SR reconstruction methods still face the following issues: (1) They require a large number of iterations to reconstruct the final image, which is inefficient and consumes a significant amount of computational resources. (2) The results reconstructed by these methods are often misaligned with the real high-resolution images, leading to remarkable distortion in the reconstructed MR images. To address the aforementioned issues, we propose an efficient diffusion model for multi-contrast MRI SR, named as DiffMSR. Specifically, we apply DM in a highly compact low-dimensional latent space to generate prior knowledge with high-frequency detail information. The highly compact latent space ensures that DM requires only a few simple iterations to produce accurate prior knowledge. In addition, we design the Prior-Guide Large Window Transformer (PLWformer) as the decoder for DM, which can extend the receptive field while fully utilizing the prior knowledge generated by DM to ensure that the reconstructed MR image remains undistorted. Extensive experiments on public and clinical datasets demonstrate that our DiffMSR outperforms state-of-the-art methods.
Rethinking the optimization process for self-supervised model-driven MRI reconstruction
University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；Shenzhen University（深圳大学）；Nanchang University（南昌大学）
Rethinking Convergence in Deep Learning: The Predictive-Corrective Paradigm for Anatomy-Informed Brain MRI Segmentation
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Key Laboratory of Signal Detection and Processing, Xinjiang University（新疆大学信号检测与处理重点实验室）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction
Department of Radiology, Northwestern University（放射科，西北大学）；Department of Biomedical Engineering, Huazhong University of Science and Technology（生物医学工程系，华中科技大学）
Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
Department of Computer Science, Durham University（杜伦大学计算机科学系）；College of Computer Science and Engineering, Dalian Minzu University（大连民族大学计算机科学与工程学院）；Department of Automation, Tsinghua University（清华大学自动化系）；Jarvis Research Center, Tencent YouTu Lab（腾讯YouTu实验室 Jarvis 研究中心）；School of Computing, Northumbria University（北爱尔兰大学 computing 学院）；SunwayAI Research Lab, Fuyang Normal University（阜阳师范学院 SunwayAI 研究实验室）；Chinese Academy of Sciences R&D Center for Internet of Things（中国科学院物联网研究发展中心）
Rethinking Image Super-Resolution from Training Data Perspectives
National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所（AIST））；Keio University（庆应义塾大学）；University of Tsukuba（筑波大学）；University of Amsterdam（阿姆斯特丹大学）；University of Oxford（牛津大学）；Tokyo Institute of Technology（东京工业大学）
Rethinking the Localization in Weakly Supervised Object Localization
Wuhan University（武汉大学）；Hubei Luojia Laboratory（湖北珞珈实验室）；Beijing Institute of Technology（北京理工大学）；City, University of London（伦敦大学城市学院）；Nanyang Technological University（南洋理工大学）
Rethinking Alignment in Video Super-Resolution Transformers
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Sydney（悉尼大学）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent PCG（腾讯平台与内容事业群）
DroneSR: Rethinking Few-shot Thermal Image Super-Resolution from Drone-based Perspective
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；Guangdong Power Grid, Ltd.（广东电网有限公司）；School of Computer Science, Sun Yat-sen University（中山大学计算机学院）
The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation
Nankai University（南开大学）；South China University of Technology（华南理工大学）；Peking University（北京大学）；Henan Institute of Science and Technology（河南科技学院）；MicroBT Electronics Technology Co., Ltd（深圳比特微电子科技有限公司）
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout
School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学生物医学工程系）；Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford（牛津大学整合神经影像中心）；Department of Radiology & Biomedical Imaging, University of California San Francisco（加州大学旧金山分校放射科与生物医学成像系）
Coupled-Projection Residual Network for MRI Super-Resolution
Harbin Institute of Technology（哈尔滨工业大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Nanyang Technological University（南洋理工大学）；Shenzhen University（深圳大学）；Inception Institute of Artificial Intelligence（因赛普申人工智能研究院）
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction
Stanford University School of Medicine（斯坦福大学医学院）；Xiamen University（厦门大学）；School of Electronic Science and Engineering, Xiamen University（厦门大学电子科学与工程学院）；School of Informatics, Xiamen University（厦门大学信息学院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（马萨诸塞州总医院）；Harvard Medical School（哈佛医学院）；Boston University（波士顿大学）；Harvard University（哈佛大学）
