3 pages, 8 figures, Procedding of NIDays 2003 (sponsored by National Instruments), Rome 2003. Winner (2nd classified) of the price "Best Application of Measurement and Automation
A very complex vision system is developed to detect luminosity variations connected with the discovery of new planets in the Universe. The traditional imaging system can not manage a so large load. A private net is implemented to perform an automatic vision and decision architecture. It lets to carry out an on-line discrimination of interesting events by using two levels of triggers. This system can even manage many Tbytes of data per day. The architecture avails itself of a distributed parallel network system based on a maximum of 256 standard workstations with Microsoft Window as OS.
Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging
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）；Nanchang University（南昌大学）
Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications
Sun Yat-sen University（中山大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Peking Union Medical College 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北京协和医院）；University of Portsmouth（朴茨茅斯大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）
PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes 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）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application（广东省医学图像分析与应用人工智能重点实验室）；National Center for Applied Mathematics Shenzhen (NCAMS)（深圳国家应用数学中心）；School of Physics and Optoelectronics, Xiangtan University（湘潭大学物理与光电工程学院）
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Department of Electrical \& Computer Engineering, University of Minnesota, MN, USA Center for Magnetic Resonance Research, University of Minnesota, MN, USA
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
Zhejiang Shuren University（浙江树人大学）；Imperial College London（伦敦帝国理工学院）；Nantong University（南通大学）；Yantai University（烟台大学）；Peking University School and Hospital of Stomatology（北京大学口腔医学院）；Peking University（北京大学）；TECNALIA；Basque Research and Technology Alliance（巴斯克研究与技术联盟）；University of the Basque Country（巴斯克大学）；The First Affiliated Hospital of Shenzhen University Health Science Center（深圳大学医学部第一附属医院）；Houston Methodist Cancer Center（休斯顿卫理公会癌症中心）；Houston Methodist Hospital（休斯顿卫理公会医院）；Weill Cornell Medicine（威尔康奈尔医学院）
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representation
Department of Neuroradiology, University Hospital Heidelberg（神经放射科，海德堡大学医院）；School of Electrical Engineering and Automation, Wuhan University（电气工程与自动化学院，武汉大学）；Momoni AI；Department of Stomatology, Shenzhen People’s Hospital（口腔科，深圳人民医院）；College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；Department of Game Design, Faculty of Arts, Uppsala University（游戏设计系，艺术学院，乌普萨拉大学）；School of Computer Science, University of Sydney（计算机科学学院，悉尼大学）；School of Biomedical Engineering, The University of Sydney（生物医学工程学院，悉尼大学）
提出一种基于广义 INR 的 MRI 并行成像重建框架，利用尺度嵌入编码器和体素先验渲染全采样 k 空间，在 4–6 倍加速下提升质量并降低计算开销。
Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning
Vanderbilt University（范德堡大学）；Vanderbilt University Institute of Imaging Science（范德堡大学影像科学研究所）；Vanderbilt University Medical Center（范德堡大学医学中心）；Oak Ridge National Laboratory（橡树岭国家实验室）
