Development of fast deep learning quantification for magnetic resonance fingerprinting in vivo.

Journal: Magnetic resonance imaging
Published Date: Apr 7, 2020

Abstract

PURPOSE: A deep neural network was developed for magnetic resonance fingerprinting (MRF) quantification. This study aimed at extending previous studies of deep learning MRF to in vivo applications, allowing sub-second computation time for large-scale data.

Authors

  • Peng Cao
    Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.
  • Di Cui
    Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
  • Vince Vardhanabhuti
    Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
  • Edward S Hui
    Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China. Electronic address: edward.s.hui@gmail.com.

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