Anja Brau1, Peng Lai1, Srihari
Narasimhan2, Babu Narayanan3, Vijaya Saradhi2
1Global Applied Science
Laboratory, GE Healthcare, Menlo Park, CA, United States; 2Computing
& Decision Sciences Lab, GE Global Research, Bangalore, India; 3Medical
Image Analysis Lab, GE Global Research, Bangalore, India
As part of the calibration step for Compressed Sensing & Parallel Imaging algorithms like ESPIRiT and L1-SPIRiT, computation of kernel weights involves obtaining a least squares fit for predicting target points in the calibration region using a set of source points in their neighborhood, separately for each coil. If we do not use the coil neighbors of the target location, the linear system needs to be solved only once. We observe that using this approach, we get significant computational benefit and still obtain similar image quality for high channel count reconstructions.