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.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
