Meeting Banner
Abstract #2657

ESPIRIT-Based Coil Compression for Cartesian Sampling

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.

Keywords

accelerated acceleration achieve adding applied arises arrays audience auto basis best calibration channel channels coefficients coil coils color combine combined combining complex component compress compressed compresses compressing compression computation computationally compute computed computer conducted confirming conjugation constant constructed count dataset datasets demanding depicts deviation dimensions eigenvalue electrical employ engineering engineers equivalent error estimating existing expectation expected extracting faster fewer forward fully function gives globally hardly head improved inverse just kernel like locally many maps martin matrix meets multiplication noise noiseless noisy normalized offering ones operators optimal original parallel pixel pixels practice principal process processing produce produces proposed receiver reconstruction reduce remaining represent resilient respectively restricting robust robustness root sampled sampling scheme schemes sciences sections selected sense sensitivity sets simulation sliding software space spatially speeding speedup square squared squares submitted subspace suffers suggests susceptible synthesized target theory thereby transforming trials utilizes various vary varying virtual virtue wise worlds