Synthesizing diffusion tensor imaging from functional MRI using fully convolutional networks.

Journal: Computers in biology and medicine
Published Date: Oct 31, 2019

Abstract

PURPOSE: Medical image synthesis can simulate a target modality of interest based on existing modalities and has the potential to save scanning time while contributing to efficient data collection. This study proposed a three-dimensional (3D) deep learning architecture based on a fully convolutional network (FCN) to synthesize diffusion-tensor imaging (DTI) from resting-state functional magnetic resonance imaging (fMRI).

Authors

  • Seong-Jin Son
    Department of Electronic and Computer Engineering, Sungkyunkwan University, South Korea; Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, South Korea; NEUROPHET Inc., South Korea.
  • Bo-Yong Park
    Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea.
  • Kyoungseob Byeon
    Department of Electronic and Computer Engineering, Sungkyunkwan University, South Korea; Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, South Korea.
  • Hyunjin Park
    Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.

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