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Abstract #1352

DeepResp: Deep Neural Network for respiration-induced artifact correction in 2D multi-slice GRE

Hongjun An1, Hyeong-Geol Shin1, Sooyeon Ji1, Woojin Jung1, Sehong Oh2, Dongmyung Shin1, Juhyung Park1, and Jongho Lee1
1Department of Electrical and computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Korea, Republic of

Respiration-induced B0 fluctuation can generate artifacts by inducing phase errors. In this study, a new deep-learning method, DeepResp, is proposed to correct for the artifacts in multi-slice GRE images without any modification in sequence or hardware. DeepResp is designed to extract the phase errors from a corrupted image using deep neural networks. This information was applied to k-space data, generating an artifact-corrected image. When tested, DeepResp successfully reduced the artifacts of in-vivo images, showing improvements in normalized-root-mean-square error (deep breathing: from 13.9 ± 4.6% to 5.8 ± 1.4%; natural breathing: from 5.2 ± 3.3% to 4.0 ± 2.5%).

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