Journal of Guangdong University of Technology ›› 2020, Vol. 37 ›› Issue (06): 26-31.doi: 10.12052/gdutxb.200011

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Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning

Xia Hao1, Cai Nian1, Wang Ping2, Wang Han3   

  1. 1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. Department of Hepatobiliary Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China;
    3. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-01-10 Online:2020-11-02 Published:2020-11-02

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Abstract: High-resolution magnetic resonance images are of great significance for medical diagnosis. A convolutional neural network with multi-resolution learning is proposed for magnetic resonance image (MR) super-resolution. The network is an improved deep residual network, which involves residual units for feature extraction, a deconvolution layer for multi-resolution up-sampling, and a multi-resolution learning layer. The proposed network performs the super-resolution task in the low-resolution space, which can accelerate the network. Multi-resolution upsampling is put forward to integrate multiple residual unit information and to accelerate the network. Multi-resolution learning can adaptively determine the contributions of these upsampled high-dimensional feature maps to high-resolution MR image reconstruction. Experiment results indicate that the proposed method can achieve a good super-resolution reconstruction performance for magnetic resonance images, which is superior to the state-of-the-art deep learning methods.

Key words: convolutional neural network, multi-resolution learning, magnetic resonance image, super-resolution reconstruction

CLC Number: 

  • TP391.4
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