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Abstract #1245

Accelerated MR parametric mapping with a hybrid deep learning model

Haoxiang Li1,2, Jing Chen1, Yuanyuan Liu1, Hairong Zheng1, Dong Liang1, and Yanjie Zhu1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China

Magnetic Resonance (MR) parametric mapping like $$$T_1$$$ , $$$T_2$$$, proton density is a powerful tool for biology tissue characterization, which is useful for clinical application such as diagnosis of pathologies including Alzheimer’s disease and multiple sclerosis1, evaluation of myocardial fibrosis2 and assessment of knee cartilage damage3. However the long scan time makes it challenging for practical clinical application. The purpose of this study was to develop a deep learning based method for accelerated MR parametric mapping with good performance at high acceleration rate both by reducing the contrast number and undersampling the k-space data.

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