22. Shortening acquisition time and reducing the motion-artifact are two of the most critical issues in MRI. As a promising solution, high-quality MRI image restoration provides a new approach to achieve higher resolution without costing additional acquisition time or modification on the pulse sequences. Recently, as to the rise of deep learning, convolutional neural networks have been proposed for super-resolution (SR) image generation and motion-artifact reduction (MAR) for MRI. Recent studies suggest that using perceptual feature space loss and k space loss to capture the perceptual information and high-frequency information of images, respectively. However, the quality of reconstructed SR and MAR MR images is limited because the most important details of the informative area in the MR image, the edges and the structure, cannot be very well restored. Besides, lots of the SR approaches are trained by using low-resolution images generated by applying bicubic or blur-downscale degradation, which cannot represent the real process of MRI measurement. Such inconsistencies lead to performance degradation in the reconstruction of SR images as well. This study reveals that using the L1 loss of SSIM and gradient map edge quality loss could force the deep learning model to focus on studying the features of edges and structure details of MR images, thus generating SR images with more accurate, fruitful information and reduced motion-artifact. We employed a state-of-the-art model, RCAN, as the network framework in both SR and MAR tasks, trained the model by using low-resolution images and motion-artifact affected images which were generated by emulating how they are measured in the real MRI measurement to ensure the model can be easily applied in the practical clinic environment, and verified the trained model could work fairly well.
MR-Compass: Inertial Navigation-Driven Motion Correction for Brain MRI
Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital（波士顿儿童医院放射科计算放射学实验室）；Department of Radiology, Harvard Medical School（哈佛医学院放射系）；Siemens Medical Solutions（西门子医疗解决方案）；BIOPAC Systems Inc.（BIOPAC系统公司）
Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines
Dept. of Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, USA（放射学与成像科学系，犹他大学，盐湖城，UT，USA）；Dept. of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT, USA（电气与计算机工程系，犹他大学，盐湖城，UT，USA）；Dept. of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（计算机视觉系，莫扎德人工智能大学，阿布扎赫，EUA）；Dept. of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA（生物医学工程系，犹他大学，盐湖城，UT，USA）；Dept. of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA（电气与计算机工程系，华盛顿大学，西雅图，WA，USA）
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Tampere University（坦佩雷大学）；Qatar University（卡塔尔大学）；VTT Technical Research Centre of Finland（芬兰国家技术研究中心）；Hamad Medical Corporation Hospital（哈马德医疗公司医院）；Hayatabad Medical Complex（海亚特阿巴德医疗中心）
Deep learning-based reconstruction of highly accelerated 3D MRI
GE Research（通用电气研究院）；GE Healthcare（通用电气医疗）；Mayo Clinic（妙佑医疗国际）；Walter Reed National Military Medical Center（沃尔特里德国家军事医疗中心）；Uniformed Services University（美国三军卫生科学大学）
AtlasGS: Brain MRI Spatial Resolution Harmonization With Shared Gaussian Geometry
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（西奈山医疗中心生物医学成像研究所）；Tsinghua Medicine, Tsinghua University（清华大学医学院）；Department of Bioengineering, University of California-Los Angeles（加州大学洛杉矶分校生物工程系）；Department of Computational Medicine, Cedars-Sinai Medical Center（西奈山医疗中心计算医学系）
Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior
University of Electronic Science and Technology of China（电子科技大学）；City University of Hong Kong（香港城市大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shenzhen Institute For Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳研究院）
MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院生物医学工程与影像研究所）；Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院诊断、分子与介入放射学系）；Rotterdam School of Management, Erasmus University Rotterdam（伊拉斯姆斯大学鹿特丹管理学院）；East River Medical Imaging（东河医学影像中心）；Department of Radiology, Cornell Medicine（康奈尔医学院放射学系）；Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院温赖克人工智能与人类健康系）
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Medicine and Health, Technical University of Munich（慕尼黑工业大学医学与健康学院）
Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study
Queen Square Institute of Neurology, University College London, London, UK（伦敦大学学院医院神经科学研究所）；National Hospital for Neurology and Neurosurgery, London, UK（伦敦神经病学与神经外科医院）；NVIDIA, UK（英国NVIDIA公司）；Great Ormond Street Hospital for Children, London, UK（伦敦儿童医院）；Royal National Orthopaedic Hospital, Stanmore, Middlesex, UK（斯坦莫尔皇家骨科医院，中西敏，英国）；University College Hospitals NHS Foundation Trust, London, UK（伦敦大学学院医院 NHS 基础信托）；Royal Free Hospital, London, UK（伦敦皇家自由医院）；Imperial College Healthcare NHS Trust, London, UK（伦敦帝国学院医疗信托）；Imperial College London, London, UK（伦敦帝国学院）
École polytechnique fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University Hospital (CHUV)（沃州大学医院）；University of Lausanne (UNIL)（洛桑大学）；Center for Biomedical Imaging (CIBM)（生物医学成像中心）
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging
University of Tuebingen（蒂宾根大学）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Stanford University（斯坦福大学）；Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI
University Hospital Basel（巴塞尔大学医院）；University of Freiburg（弗莱堡大学）；University Hospital of Tuebingen（蒂宾根大学医院）；German Cancer Research Center（德国癌症研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
