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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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