Denoising arterial spin labeling perfusion MRI with deep machine learning.

Journal: Magnetic resonance imaging
Published Date: Jan 15, 2020

Abstract

PURPOSE: Arterial spin labeling (ASL) perfusion MRI is a noninvasive technique for measuring cerebral blood flow (CBF) in a quantitative manner. A technical challenge in ASL MRI is data processing because of the inherently low signal-to-noise-ratio (SNR). Deep learning (DL) is an emerging machine learning technique that can learn a nonlinear transform from acquired data without using any explicit hypothesis. Such a high flexibility may be particularly beneficial for ASL denoising. In this paper, we proposed and validated a DL-based ASL MRI denoising algorithm (DL-ASL).

Authors

  • Danfeng Xie
    Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA.
  • Yiran Li
    University of California at Davis, Davis, CA, USA.
  • Hanlu Yang
    Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA.
  • Li Bai
    Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA.
  • Tianyao Wang
    Department of Radiology, the Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.
  • Fuqing Zhou
    Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang 330006, China; Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, China. Electronic address: fq.chou@yahoo.com.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ze Wang
    School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, 312 Anshanwest Road, Nankai District, Tianjin 300193, China.

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