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Abstract #0678

Joint Parallel Imaging reconstruction with Deep Learning for Multi-Contrast Synthetic MRI

Jae-Hun Lee1, Kanghyun Ryu1, Sung-Min Gho2, Ho-Sung Kim3, Mohammed A. Al-masni1, and Dong-Hyun Kim1
1Deparment of Electrical & Electronic Engineering, Yonsei Univ., Seoul, Korea, Republic of, 2MR Collaboration and Development, GE Healthcare, Seoul, Korea, Republic of, 3Department of Radiology, Asan medical center, Seoul, Korea, Republic of

Synthetic MRI or magnetic resonance imaging compilation (MAGiC) uses multiple-dynamic multiple-echo acquisition(MDME) and acquires 8 contrast images in a single scan. SENSE or GRAPPA method is conventionally used to reconstruct undersampled acquisition for respective contrast images. However, the method enables limited acceleration up to 2~3. In this study, combined reconstruction method (Joint Parallel Imaging with Deep Learning) is explored. The proposed method shows acceptable image quality with RMSE (4.6%) at the higher acceleration factor (up to 8) comparable to conventional GRAPPA with acceleration rate of 2~3.

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