Laplacian pyramid-based complex neural network learning for fast MR imagingDownload PDF

25 Jan 2020, 12:36 (edited 01 Jul 2020)MIDL 2020 Conference Blind SubmissionReaders: Everyone
  • Track: full conference paper
  • TL;DR: Proposed and designed a new Laplacian pyramid-based multi-scale complex neural network learning framework for fast MR imaging.
  • Keywords: Deep learning, complex convolution, Laplacian pyramid decomposition
  • Abstract: A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data. Specifically, three major contributions have been made: 1) A new framework has been proposed to explore the encouraging multi-scale properties of Laplacian pyramid decomposition; 2) A cascaded multi-scale network architecture with complex convolutions has been designed under the proposed framework; 3) Experimental validations on an open source dataset fastMRI demonstrate the encouraging properties of the proposed method in preserving image edges and fine textures.
  • Paper Type: both
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