Abstract #0432

Low Rank Matrix Completion-based Reconstruction for Undersampled Magnetic Resonance Fingerprinting Data

Mariya Doneva¹, Thomas Amthor¹, Peter Koken¹, Karsten Sommer¹, and Peter Börnert¹

¹Philips Research Europe, Hamburg, Germany

In this work, we present a method for reconstruction of undersampled Magnetic Resonance Fingerprinting (MRF) data based on low rank matrix completion, which is performed entirely in k-space and has low computational cost. The method shows significant improvement in the MRF parameter maps accuracy compared to direct matching from undersampled data, potentially enabling more robust highly accelerated MR Fingerprinting.

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