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Abstract #0167

Unsupervised correction network for Nyquist ghost artifact and geometry distortion in echo planar imaging

Jeewon Kim1, Kinam Kwon1, Seohee So2, Byungjai Kim1, Wonil Lee1, and HyunWook Park1
1Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Republic of, 2Korea Institute of Science and Technology, Seoul, Korea, Republic of


We propose a new correction scheme using a deep neural network with unsupervised learning to correct Nyquist ghosts and geometry distortions occurring in EPI. The proposed scheme includes NGAC-net and GDC-net. First, the NGAC-net estimates the phase error of k-space with the help of a ghost formulation operator and correlation loss. The NGAC-net produces two Nyquist ghost corrected images obtained by dual-polarity phase-encoding gradients. The GDC-net is trained to estimate the frequency-shift map using the two output images from the NGAC-net. Afterwards, an MR image generation operator utilizes the estimated frequency-shift map to obtain the geometry distortion corrected images.

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