Michael
Lustig1,2, Michael Elad3, John Mark Pauly2
1Electrical Engineering and Computer
Science, University of California Berkeley, Berkeley, CA, United States; 2Electrical
Engineering, Stanford University, Stanford, CA, United States; 3Computer
Science, Technion IIT, Haifa, Israel
A
new method for parallel imaging that requires no special autocalibration
lines or calibration scans is presented. Instead, the method jointly calibrates,
and synthesizes missing data from the entire acquired k-space. The proposed
method is based on low-rank matrix completion, which is an extension of the
compressed sensing theory to matrices. It is implemented by iterative
singular value thresholding. The method can be used to reconstruct
undersampled data, to generate calibration data for GRAPPA-like methods, or
just to improve calibration when the calibration area is too small.