Abstract #2870

Calibrationless Parallel Imaging Reconstruction by Structured Low-Rank Matrix Completion

Michael Lustig^1,2, 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.