Peng Lai1, Michael Lustig2,3, Shreyas S. Vasanawala4, Anja C. S. Brau1
1Global Applied Science Laboratory, GE Healthcare, Menlo Park, CA, USA; 2Electrical Engineering, Stanford University, Stanford, CA, USA; 3Electrical Engineering & Computer Science, University of California, Berkeley, CA, USA; 4Radiology, Stanford University, Stanford, CA, USA
Compressed sensing (CS) parallel imaging (PI) methods, such as L1SPIRiT, provide better image quality than CS or PI alone, but requires highly intensive iterative computation. Efficient L1SPIRiT (ESPIRiT) greatly reduces the computation intensity based on eigenvector computations. This work provides a theoretical analysis of similarities between these two approaches and demonstrates that they should converge to the same solution. Based on our analysis, we show the existence of multiple dominant eigenvectors for overlapped FOV acquisition, where original ESPIRiT generates significant artifacts like mSENSE and identify a solution. Our results based on invivo datasets showed that the proposed modified ESPIRiT can provide reconstruction very similar to L1SPIRiT regardless of FOV overlap. The modified ESPIRiT algorithm is a robust and computationally efficient solution to CS-PI reconstruction.