Abstract #0065

ESPIRiT (Efficient Eigenvector-Based L1SPIRiT) for Compressed Sensing Parallel Imaging - Theoretical Interpretation & Improved Robustness for Overlapped FOV Prescription

Peng Lai¹, Michael Lustig^2,3, 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.