Post-Cartesian Calibrationless Parallel Imaging Reconstruction by Structured Low-Rank Matrix Completion

Michael Lustig¹

An autocalibrating post-Cartesian parallel imaging method is presented. It is based on structured, low-rank matrix completion which is an extension of compressed sensing to Matrices. The method does not require a fully sampled autocalibration area in k-space. Instead it jointly calibrates and reconstructs the signal from the undersampled data alone. Results using spiral sampling are demonstrated showing similarly good reconstruction compared to method that use explicit calibration data.