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Abstract #5160

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

H. Christian M. Holme1,2, Frank Ong3, Sebastian Rosenzweig1,2, Robin N. Wilke1,2, Michael Lustig3, and Martin Uecker1,2

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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