Fast and Calibrationless Low-Rank Reconstruction through Deep Learning Estimation of Multi-Channel Spatial Support
Zheyuan Yi1,2,3, Yujiao Zhao1,2, Linfang Xiao1,2, Yilong Liu1,2, Christopher Man1,2, Jiahao Hu1,2,3, Vick Lau1,2, Alex Leong1,2, Fei Chen3, 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, 3Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
In traditional parallel imaging, calibration data need to be acquired, prolonging data acquisition time or/and sometimes increasing the susceptibility to motion. Low-rank parallel imaging has emerged as a calibrationless alternative that formulates reconstruction as a structured low-rank matrix completion problem while incurring a cumbersome iterative reconstruction process. This study achieves a fast and calibrationless low-rank reconstruction by estimating high-quality multi-channel spatial support directly from undersampled data via deep learning. It offers a general and effective strategy to advance low-rank parallel imaging by making calibrationless reconstruction more efficient and robust in practice.
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