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Abstract #0345

Efficient L1SPIRiT Reconstruction (ESPIRiT) for Highly Accelerated 3D Volumetric MRI with Parallel Imaging and Compressed Sensing

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.