Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network.

Journal: Medical physics
Published Date: May 11, 2019

Abstract

PURPOSE: In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distribution function (fODF) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultimately facilitates the understanding of brain connectivity and exploration of neurological dysfunction. Recently, multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) method has been explored for reconstructing full fODFs. To achieve a reliable fitting, similar to other model-based approaches, a large number of diffusion measurements is typically required for MSMT-CSD method. The prolonged acquisition is, however, not feasible in practical clinical routine and is prone to motion artifacts. To accelerate the acquisition, we proposed a method to reconstruct the fODF from downsampled diffusion-weighted images (DWIs) by leveraging the strong inference ability of the deep convolutional neural network (CNN).

Authors

  • Zhichao Lin
    Department of Instrument Science & Technology, Zhejiang University, Hangzhou, 310027, China.
  • Ting Gong
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Kewen Wang
    College of Natural Science, Computer Science, The University of Texas at Austin, Austin, TX, USA.
  • Zhiwei Li
    Department of Instrument Science & Technology, Zhejiang University, Hangzhou, 310027, China.
  • Hongjian He
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Qiqi Tong
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Feng Yu
    Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Forensic Identification Center of Hebei Medical University, College of Forensic Medicine, Hebei Medical University, Shijiazhuang 050017, China.
  • Jianhui Zhong
    Department of Imaging Sciences, University of Rochester, Rochester, New York.

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