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Abstract #2738

Reconstruction of MR images by combining k-spaces of multi-contrast MR data through deep learning

Won-Joon Do1, Yo Seob Han1, Seung Hong Choi2, Jong Chul Ye1, and Sung-Hong Park1

1Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea, 2Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea

We propose a new deep neural network (Y-net) that can utilize images acquired with a different MR contrast for reconstruction of down-sampled images. K-space center of down-sampled T2-weighted images and k-space edge of full-sampled T1-weighted images were combined through one Y-net, and desired high-resolution T2-weighted images were generated by another Y-net. The proposed network not only improved spatial resolution but also suppressed ringing artifacts caused by the down‑sampling at the k-space center. The developed technique potentially enables to accelerate the multi-contrast MR imaging in routine clinical studies.

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