Meeting Banner
Abstract #1360

Rigid motion artifact correction in multi-echo GRE using navigator detection and Parallel imaging reconstruction with Deep Learning

Seul Lee1, Jae-Hun Lee1, Soozy Jung1, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of

Motion artifacts which are occurred in subject motion during MR data acquisition can cause significant image degradation. In this study, we propose a rigid motion artifact correction method, which eliminates the motion-corrupted phase encoding lines detected by navigator echoes and reconstructs motion-compensated images using parallel imaging with deep learning. According to evaluation of simulated motion data and real motion-corrupted data, the proposed method achieved competent compensation for motion artifacts.

This abstract and the presentation materials are available to members only; a login is required.

Join Here