Correcting ghost in single-shot spatiotemporal encoding by deep learning

Liyang Xia; Kewen Liu; Xinjie Liu; Qingjia Bao; Chaoyang Liu

doi:10.1117/12.2679581

16 June 2023 Correcting ghost in single-shot spatiotemporal encoding by deep learning

Liyang Xia, Kewen Liu, Xinjie Liu, Qingjia Bao, Chaoyang Liu

Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 1270227 (2023) https://doi.org/10.1117/12.2679581
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China

Abstract

SPatiotemporal ENcoding (SPEN) has good robustness to inhomogeneous magnetic fields and chemical shifts. However, the eddy current and gradient delay in the acquisition process may cause a certain phase difference between the odd and even line data in the phase encoding dimension, resulting in severe ghost in the reconstructed images. This work proposes a deep learning model for correcting ghost in single-shot SPEN. Experimental results show that the proposed model can effectively correct ghost.

Citation Download Citation

Liyang Xia, Kewen Liu, Xinjie Liu, Qingjia Bao, and Chaoyang Liu "Correcting ghost in single-shot spatiotemporal encoding by deep learning", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 1270227 (16 June 2023); https://doi.org/10.1117/12.2679581

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