Articles

Reconstruction of Simultaneous Multi-Slice MRI Data by Combining Virtual Conjugate Coil Technology and Convolutional Neural Network

  • WANG Wan-ting ,
  • SU Shi ,
  • JIA Sen ,
  • LIANG Dong ,
  • WANG Hai-feng
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  • 1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received date: 2020-01-14

  Online published: 2020-04-16

Abstract

This paper proposes an image reconstruction method for simultaneous multi-slice imaging (SMS) by combining the virtual conjugate coil (VCC) technology and robust artificial-neural-networks for k-space interpolation (RAKI). This method can effectively improve the reconstruction quality, and is named VIRGINIA (VIRtual conjuGate coIls Neural-networks InterpolAtion). VIRGINIA utilizes the complex conjugate symmetry property of the virtual coil concept to generate virtual coil data for training, and obtains better image quality by applying the trained network to the original aliased SMS data. With experimental data, the VIRGINIA method was compared to other reconstruction methods (i.e., RAKI only and slice-GRAPPA) in terms of quantitative indices such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). The results demonstrated that, under some certain slice-acceleration factors, VIRGINIA produced better reconstruction quality than those obtainable by Slice-GRAPPA and RAKI.

Cite this article

WANG Wan-ting , SU Shi , JIA Sen , LIANG Dong , WANG Hai-feng . Reconstruction of Simultaneous Multi-Slice MRI Data by Combining Virtual Conjugate Coil Technology and Convolutional Neural Network[J]. Chinese Journal of Magnetic Resonance, 2020 , 37(4) : 407 -421 . DOI: 10.11938/cjmr20202800

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