Abstract #5160

ENLIVE: A Non-Linear Calibrationless Method for Parallel Imaging using a Low-Rank Constraint

H. Christian M. Holme^1,2, Frank Ong³, Sebastian Rosenzweig^1,2, Robin N. Wilke^1,2, Michael Lustig³, and Martin Uecker^1,2

¹Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany, ²partner site Göttingen, DZHK (German Center for Cardiovascular Research), Göttingen, Germany, ³Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States

We propose an extension to Regularized Non-Linear Inversion (NLINV), which simultaneously reconstructs multiple images and sets of coil sensitivity profiles. This method, termed ENLIVE (Extended Non-Linear InVersion inspired by ESPIRiT), can be related to a convex relaxation of the NLINV problem subject to a low-rank constraint. From NLINV, it inherits its suitability for calibrationless and non-Cartesian imaging; from ESPIRiT it inherits robustness to data inconsistencies.

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