Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial networks (GANs), which have proven to be quite useful in generating sharp-looking details in SR. We introduce a conditional GAN with perceptual loss, which is conditioned upon the input low-resolution image, which improves the performance for isotropic and anisotropic MRI super-resolution.
Comparative Analysis of GAN and Diffusion for MRI-to-CT translation
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark（哥本哈根大学计算机科学系）；Department of Oncology, Rigshospitalet, Copenhagen, Denmark（哥本哈根医院肿瘤科）
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Department of Mathematics and Systems Engineering（数学与系统工程系）；Department of Biomedical Engineering & Science（生物医学工程与科学系）；Florida Institute of Technology（佛罗里达理工学院）；A. A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；Department of Radiology（放射科）；Embry-Riddle Aeronautical University（艾姆伯-里尔航空大学）
MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Jiangsu Institute of Hematology（江苏血液学研究所）；The First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；University Sorbonne Paris Nord（巴黎北索邦大学）；Henan University of Chinese Medicine（河南中医药大学）；German Cancer Research Center（德国癌症研究中心）；Athinoula A. Martinos Centre for Biomedical Imaging（阿西诺拉·A.马蒂诺斯生物医学成像中心）；Geneva University Hospital（日内瓦大学医院）；IDP Institute（IDP研究所）；University of Orleans（奥尔良大学）
GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising
College of Life Science and Technology, Huazhong University of Science and Technology（华中科技大学生命科学技术学院）；New York University Shanghai（上海纽约大学）；School of Life Sciences and Biotechnology, Shanghai Jiao Tong University（上海交通大学生命科学技术学院）
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤科和Winship癌症研究所，埃默里大学）；Department of Radiology, Mayo Clinic（放射科，梅奥诊所）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences（中国科学院大学深圳先进技术学院）；Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）
Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI
School of Computation, Information and Technology（计算信息学院）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；School of Medicine, Klinikum Rechts der Isar（医学院，莱茵河右岸医院）；Department of Quantitative Biomedicine（定量生物医学系）；University of Zurich（苏黎世大学）；Department of Computing（计算系）；Imperial College London（伦敦帝国理工学院）
U-Net-Based Generative Joint Source-Channel Coding for Wireless Image Transmission
Singapore University of Technology and Design（新加坡科技设计大学）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Southeast University（东南大学）；School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）
A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline
Department of Nanobiophysics, Faculty of Science and Technology, University of Twente（特文特大学科学与技术学院生物医学系）；Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute（荷兰癌症研究所手术引导外科部）；Netherlands Cancer Institute（荷兰癌症研究所）；Center for Early Cancer Detection, Netherlands Cancer Institute（荷兰癌症研究所早期癌症检测中心）
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives
Imperial College London（伦敦帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Yantai University（烟台大学）；University of Cambridge（剑桥大学）；Beijing Institute of Technology（北京理工大学）
LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior
Shenzhen Technology University（深圳科技大学）；Shenzhen University（深圳大学）；Huawei（华为）；Nanjing University of Science and Technology（南京理工大学）；Qilu University of Technology（齐鲁工业大学）
Diffusion-Based Synthesis of 3D T1w MPRAGE Images from Multi-Echo GRE with Multi-Parametric MRI Integration
Department of Radiology and Imaging Sciences, Emory University（埃默里大学放射学与成像科学系）；School of Biological Sciences, Georgia Institute of Technology（佐治亚理工学院生物科学学院）
HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restoration
Hawkes Institute, UCL（UCL哈维斯研究所）；Dept. of Medical Physics and Biomedical Engineering, UCL（UCL医学物理与生物医学工程系）；Dept. of Computer Science, UCL（UCL计算机科学系）；Centre for AI, DS&AI, AstraZeneca, UK（阿斯利康AI中心）
FS-RWKV: Leveraging Frequency Spatial-Aware RWKV for 3T-to-7T MRI Translation
Faculty of Science and Technology, University of Macau（澳门大学科学与技术学院）；School of Electronic and Communication Engineering, Shenzhen Polytechnic University（深圳职业技术学院电子与通信工程学院）；Department of Cardiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China（广东省人民医院心内科（广东省医学科学院））；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China（广东省心血管病研究院，广东省人民医院，广东省医学科学院，广州，中国）
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
University of California at Berkeley（加州大学伯克利分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；GE Global Research（通用电气全球研究院）；International Computer Science Institute（国际计算机科学研究所）
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
Center for Biomedical Imaging（生物医学成像中心）；Siemens Healthineers International AG（西门子医疗国际公司）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；sitem-insel；University of Bern（伯尔尼大学）
Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning
University of Tehran（德黑兰大学）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Research Institute for Ophthalmology and Vision Science（眼科与视觉科学研究所）
MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging
Department of Biomedical Engineering, Wake Forest University School of Medicine（生物医学工程系，威克森林大学医学院）；Department of Radiology, Wake Forest School of Medicine（放射学系，威克森林医学院）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（放射学与生物医学成像系，耶鲁医学院）
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images
University College London（伦敦大学学院）；Institute of Cardiovascular Science（心血管科学研究所）；Centre for Medical Imaging（医学影像中心）；Frimley Health NHS Trust（弗林利健康 NHS 信托基金）；UCL NHS Trust（伦敦大学学院 NHS 信托基金）
