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Abstract #3652

Accelerate Single-Channel MRI by Exploiting Uniform Undersampling Aliasing Periodicity through Deep Learning

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


Conventional parallel imaging methods mostly utilize the spatial encoding by array of receiver coils to unfold the periodic aliasing artifact resulted from uniformly undersampled k-space data. In scenarios such as low- or ultra-low-field MRI where effective receiver arrays do not exist and SNRs are low, these methods are not generally applicable. This study presents a U-Net based deep learning approach to single-channel MRI acceleration that unfolds the aliasing by exploiting its periodicity. The results demonstrate the aliasing unfolding capability of this method for single-channel MRI even at very high acceleration and in presence of pathologies.

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