Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

Journal: Annals of clinical and translational neurology
Published Date: Apr 18, 2020

Abstract

OBJECTIVE: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods.

Authors

  • Zezhong Ye
    Artificial Intelligence in Medicine (AIM) Program, Harvard Medical School, Boston, Massachusetts, USA.
  • Ajit George
    Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110.
  • Anthony T Wu
    Department of Biomedical Engineering, Washington University, St. Louis, Missouri, 63130.
  • Xuan Niu
    Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110.
  • Joshua Lin
    Keck School of Medicine, University of Southern California, Los Angeles, California, 90033.
  • Gautam Adusumilli
    Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110.
  • Robert T Naismith
    Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110.
  • Anne H Cross
    Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110.
  • Peng Sun
    Department of Microelectronics, Nankai University, Tianjin, 300350, PR China.
  • Sheng-Kwei Song
    Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110.

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