Peng
Lai1, Michael Lustig2,3, Anja CS. Brau1,
Shreyas Vasanawala4, Philip J. Beatty1, Marcus Alley2
1Applied Science Laboratory, GE
Healthcare, Menlo Park, CA, United States; 2Electrical
Engineering, Stanford University, Stanford, CA, United States; 3Electrical
Engineering and Computer Science, University of California, Berkeley, CA,
United States; 4Radiology, Stanford University, Stanford, CA,
United States
Conventional
L1SPIRiT reconstruction enables highly-accelerated MRI by combining parallel
imaging and compressed sensing but suffers from impractically long
reconstruction time. This work developed a new efficient L1SPIRiT algorithm
(ESPIRiT) to address the computation challenge from three perspectives: 1.
reducing the computation complexity based on Eigenvector calculations, 2.
reducing the number of pixels to process based on pixel-specific convergence,
3. reducing the number of iterations using parallel imaging initialization.
ESPIRiT was compared with L1SPIRiT on in-vivo datasets. Our results show that
ESPIRiT can improve image quality and reconstruction accuracy with >10
faster computation compared to L1SPIRiT.
Keywords