Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning
Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤学部和Winship癌症研究所，埃默里大学）；Department of Radiology, Mayo Clinic（放射科，梅奥诊所）；Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University（放射科和成像科学以及Winship癌症研究所，埃默里大学）；Department of Radiation and Cellular Oncology, University of Chicago（放射学和细胞肿瘤学部，芝加哥大学）
Mojtaba Safari, Shansong Wang, Vanessa L Wildman, Mingzhe Hu, Zach Eidex, Chih-Wei Chang, Erik H Middlebrooks, Richard L. J Qiu, Pretesh Patel, Ashesh B. Jani, … 展开作者
Mojtaba Safari, Shansong Wang, Vanessa L Wildman, Mingzhe Hu, Zach Eidex, Chih-Wei Chang, Erik H Middlebrooks, Richard L. J Qiu, Pretesh Patel, Ashesh B. Jani, Hui Mao, Zhen Tian, Xiaofeng Yang
Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep learning methods face fidelity-efficiency trade-offs. Purpose: To develop a computationally efficient and accurate deep learning framework for MRI SR that preserves anatomical detail for clinical integration. Materials and Methods: We propose a novel SR framework combining multi-head selective state-space models (MHSSM) with a lightweight channel MLP. The model uses 2D patch extraction with hybrid scanning to capture long-range dependencies. Each MambaFormer block integrates MHSSM, depthwise convolutions, and gated channel mixing. Evaluation used 7T brain T1 MP2RAGE maps (n=142) and 1.5T prostate T2w MRI (n=334). Comparisons included Bicubic interpolation, GANs (CycleGAN, Pix2pix, SPSR), transformers (SwinIR), Mamba (MambaIR), and diffusion models (I2SB, Res-SRDiff). Results: Our model achieved superior performance with exceptional efficiency. For 7T brain data: SSIM=0.951+-0.021, PSNR=26.90+-1.41 dB, LPIPS=0.076+-0.022, GMSD=0.083+-0.017, significantly outperforming all baselines (p<0.001). For prostate data: SSIM=0.770+-0.049, PSNR=27.15+-2.19 dB, LPIPS=0.190+-0.095, GMSD=0.087+-0.013. The framework used only 0.9M parameters and 57 GFLOPs, reducing parameters by 99.8% and computation by 97.5% versus Res-SRDiff, while outperforming SwinIR and MambaIR in accuracy and efficiency. Conclusion: The proposed framework provides an efficient, accurate MRI SR solution, delivering enhanced anatomical detail across datasets. Its low computational demand and state-of-the-art performance show strong potential for clinical translation.
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University（中南大学机器视觉与智能医学湖南省工程研究中心）；Department of Nuclear Medicine, Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心核医学科）；University of Rouen-Normandy（鲁昂-诺曼底大学）
MambaVSR: Content-Aware Scanning State Space Model for Video Super-Resolution
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；Visual Intelligence + X International Cooperation Joint Laboratory of MOE, Beijing Jiaotong University（教育部视觉智能+X国际合作联合实验室）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）
RCL-Mamba: A Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography
Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University（重庆大学光电技术及系统教育部重点实验室）；Engineering Research Center of Industrial CT Nondestructive Testing, Ministry of Education, Chongqing University（重庆大学工业CT无损检测教育部工程研究中心）
Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；College of Electronic Science, Air Force Aviation University（空军航空大学电子科学学院）；Intelligent Visual Information Perception Laboratory, Institute of Advanced Technology, Nanjing University of Posts and Telecommunications（南京邮电大学先进技术研究院智能视觉信息感知实验室）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion
University of Science and Technology of China（中国科学技术大学）；Institute of Intelligent Machines（智能机器研究所）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）；Intelligent Agriculture Engineering Laboratory of Anhui Province, Institute of Intelligent Machines（安徽省智能农业工程实验室，智能机器研究所）
Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
School of Information Science and Engineering, Huaqiao University（华侨大学信息科学与工程学院）；School of Mechanical Engineering and Automation, Huaqiao University（华侨大学机械工程与自动化学院）；School of Engineering, Huaqiao University（华侨大学工程学院）；School of Optoelectronic and Communication Engineering, Xiamen University of Technology（厦门理工大学光电子与通信工程学院）；Artificial Intelligence Institute, Hangzhou Dianzi University（杭州电子科技大学人工智能学院）
Trajectory-aware Shifted State Space Models for Online Video Super-Resolution
Pengcheng Labartory（鹏城实验室）；University of Bristol（英国布里斯托大学）；University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Graduate School, Peking University（北京大学深圳研究生院）
Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
Department of Electrical and Computer Engineering, Sungkyunkwan University（电气与计算机工程系，全北大学）；Department of Artificial Intelligence, Sungkyunkwan University（人工智能系，全北大学）；Hanwha Systems, Republic of Korea（韩华系统，韩国）；Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）
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
Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）
SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Biomedical Perception & Intelligence Lab, University of South Dakota（南达科他大学生物医学感知与智能实验室）；Aalto University（阿尔托大学）；Malaviya National Institute of Technology Jaipur（马拉维亚国家理工学院斋浦尔分校）；Machine & Hybrid Intelligence Lab, Northwestern University（西北大学机器与混合智能实验室）
Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
Faculty of Engineering and Information Technology, University of Technology Sydney, Australia（工程与信息技术学院，悉尼技术大学，澳大利亚）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China（深圳先进技术研究所，中国科学院，中国）；College of Computer Science and Technology, Qingdao University, China（计算机科学与技术学院，青岛大学，中国）；Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, China（计算机科学与控制工程学院，深圳先进技术大学，中国）
Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation
School of Digital Industry, Jiangxi Normal University（江西师范大学数字产业学院）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction
School of Basic Medical Science, Fudan University（复旦大学基础医学院）；Academy of Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东计算机科学中心（国家济南超级计算中心））
UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Johns Hopkins University（约翰霍普金斯大学）；Stanford University（斯坦福大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Adelaide（阿德莱德大学）
SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science, Peking University（北京大学计算机学院）
MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion
The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Stanford University（斯坦福大学）
MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation
School of Biomedical Engineering & Imaging Sciences, King’s College London, United Kingdom（生物医学工程与成像科学学院，伦敦国王学院，英国）；Department of Radiology, Guy’s and St Thomas’ NHS Foundation Trust, United Kingdom（放射科，盖茨和圣Thomas国家卫生信托基金会，英国）
D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removal
School of Artificial Intelligence, Hebei University of Technology（人工智能学院，河北工业大学）；School of Electrical and Computer Engineering, University of Texas at Austin（电气与计算机工程学院，德克萨斯大学奥斯汀分校）；School of Health Sciences and Biomedical Engineering, Hebei University of Technology（健康科学与生物医学工程学院，河北工业大学）
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋学国家重点实验室）；School of Cyber Science and Technology, Sun Yat-Sen University（中山大学信息科学与技术学院）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstruction
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Big Data Institute, Central South University（大数据研究院，中南大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（深圳先进技术研究所，中国科学院）；Department of Orthopaedics, The Second Xiangya Hospital of Central South University（中南大学湘雅医院骨科部）
FU-Mamba: A Frequency-Enhanced Dynamic Scanning Framework for Oralscan Image Segmentation
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；Tongxiang Hospital of Traditional Chinese Medicine（桐乡市中医院）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）；Qena University（奎纳大学）；Tampere University（坦佩雷大学）；Universidade Federal Fluminense（弗卢米嫩塞联邦大学）；Federal University of Ceara（塞阿拉联邦大学）；Firat University（菲拉特大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；AGH University of Krakow（克拉科夫AGH大学）
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification
Nanjing University of Information Science and Technology（南京信息工程大学）；Institute of Optics and Electronics（光学电子研究所）；China University of Geosciences（中国地质大学）；University of Extremadura（埃斯特雷马杜拉大学）
SwinMamba: A hybrid local-global mamba framework for enhancing semantic segmentation of remotely sensed images
Xi'an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；Nanyang Technological University（南洋理工大学）；SJTUSME-COSMOPlat Joint Research Center for New Generation Industrial Intelligent Technology（上海交通大学机械与动力工程学院-卡奥斯新一代工业智能技术联合研究中心）
