Dara Bahri1, Martin Uecker1, Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
While receiver arrays with many channels can increase parallel imaging acceleration and provide high signal-to-noise, processing the large datasets they produce is computationally demanding. Coil compression algorithms reduce, and denoise in the process, data from many coils into fewer virtual ones. Huang et al. proposed using principal component analysis to globally compress multi-coil k-space data. Zhang et al. developed an improved technique for Cartesian sampling by compressing locally along fully-sampled directions, but the method suffers in low-SNR sections of k-space. In this work we present an algorithm that compresses locally while remaining noise-robust.