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. 2016 Apr:2016:514-517.
doi: 10.1109/ISBI.2016.7493320. Epub 2016 Jun 16.

ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING

Shanshan Wang  1 , Zhenghang Su  2 , Leslie Ying  3 , Xi Peng  1 , Shun Zhu  1 , Feng Liang  4 , Dagan Feng  5 , Dong Liang  1
Affiliations

ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING

Shanshan Wang et al. Proc IEEE Int Symp Biomed Imaging. 2016 Apr.

Abstract

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and effective imaging.

Keywords: Deep learning; convolutional neural network; magnetic resonance imaging; prior knowledge.

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Figures

Fig. 1.
Fig. 1.
The flowchart of the proposed method
Fig. 2.
Fig. 2.
(a) Ground truth image; (b) 1 D central low-frequency sampling mask with acceleration factor of 3; (c) 2D poisson undersampling mask with acceleration factor of 5; (d)(e)(f) the zero-filled MR image, network output and reconstruction result from 1D undersampled data; (g)(h)(i) the zero-filled M-R image, network output and reconstruction result from 2D undersampled data.
Fig. 3.
Fig. 3.
The test results on another sagittal brain image at an acceleration factor of 3
See this image and copyright information in PMC

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