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Abstract #4355

Advancing RAKI Parallel Imaging Reconstruction with Virtual Conjugate Coil and Enhanced Non-Linearity

Christopher Man1,2, Zheyuan Yi1,2, Vick Lau1,2, Jiahao Hu1,2, Yujiao Zhao1,2, Linfang Xiao1,2, Alex T.L. Leong1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China


RAKI is recently proposed as a deep learning version of GRAPPA, which trains on auto-calibration signal (ACS) to estimate the missing k-space data. However, RAKI requires a larger amount of ACS for training and reconstruction due to its multiple convolutions which resulting in lower effective acceleration. In this study, we propose to incorporate the virtual conjugate coil and enhanced non-linearity into the RAKI framework to improve the noise resilience and artifact removal at high effective acceleration. The results demonstrate that such strategy is effective and robust at high effective acceleration and in presence of pathological anomaly.

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