Abstract #0745

P-LORAKS: Low-rank modeling of local k -space neighborhoods with parallel imaging data

Jingwei Zhuo ^1,2 and Justin P. Haldar ¹

¹ Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States, ² Electronic Engineering, Tsinghua University, China

This work presents P-LORAKS, a novel approach to constrained image reconstruction from parallel imaging data. Similar to the original LORAKS (low-rank matrix modeling of local k -space neighborhoods) method, P-LORAKS uses low-rank matrix models to generate parsimonious constrained reconstruction representations of images with limited spatial support and/or slowly varying phase. Combining LORAKS with parallel imaging data leads to further improvements in image reconstruction quality. Results are illustrated with real data, where P-LORAKS compares favorably to existing parallel imaging methods like SPIRiT and SAKE.

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