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SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

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第一作者： Hongyu,Li

第一单位： Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA.

作者： Hongyu,Li ^[1] ; Mingrui,Yang ^[2] ; Jee Hun,Kim ^[2] ; Chaoyi,Zhang ^[1] ; Ruiying,Liu ^[1] ; Peizhou,Huang ^[3] ; Dong,Liang ^[4] ; Xiaoliang,Zhang ^[3] ; Xiaojuan,Li ^[2] ; Leslie,Ying ^[5]

作者单位： Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA. ^[1] Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA. ^[2] Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, New York, USA. ^[3] Paul C. Lauterbur Research Center for Biomedical Imaging, Medical AI Research Center, SIAT, CAS, Shenzhen, China. ^[4] Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA.;Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, New York, USA. ^[5]

关键词 MR parameter mapping convolutional neural network deep learning fast relaxometry image reconstruction joint maps k-space undersampling parameter-space undersampling

医学主题词磁共振成像(Magnetic Resonance Imaging);回顾性研究(Retrospective Studies);前瞻性研究(Prospective Studies);加速度(Acceleration);图像处理, 计算机辅助(Image Processing, Computer-Assisted);算法(Algorithms)

DOI 10.1002/mrm.29411

PMID 36128884

发布时间 2024-01-02

基金项目

K25 AR078928/AR/NIAMS NIH HHS/United States R01 AR077452/AR/NIAMS NIH HHS/United States U01 EB023829/EB/NIBIB NIH HHS/United States

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