A hybrid convolutional neural network for super-resolution reconstruction of MR images.

Journal: Medical physics
Published Date: Apr 27, 2020

Abstract

PURPOSE: Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High-resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal-to-noise ratios (SNRs). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)-based super-resolution (SR) algorithms have flourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance.

Authors

  • Yingjie Zheng
    Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Bowen Zhen
    Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Aichi Chen
    Department of Radiology, University of California Los Angeles, Los Angeles, CA, 90095, USA.
  • Fulang Qi
    Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Xiaohan Hao
    Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Bensheng Qiu
    Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China.

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