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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