Zhongyuan Bi1, Martin Uecker2, Dengrong Jiang3, Michael Lustig2, Kui Ying3
1Biomedical Engineering, Tsinghua University, Beijing, China; 2Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, United States; 3Engineering Physics, Tsinghua University, Beijing, China
Auto-calibration parallel imaging (acPI) is based on local correlations in k-space. It is known to perform robustly in practice, especially when accurate sensitivity information is hard to obtain. However, corruption of ACS data, e.g. by motion, often leads to serious artifacts in the reconstructed images. In this work, we propose to exploit the redundancy in k-space to detect and correct sparse corruptions in ACS data, which could result from random, time-limited motion in clinical practice (e.g. swallowing, jerk, etc). Our work is based on low-rank matrix completion with sparse errors.