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Abstract #3860

Multi-Compartment MR Fingerprinting via Reweighted-l1-norm Regularization

Sunli Tang1, Jakob Asslaender2, Lee Tannenbaum3, Riccardo Lattanzi2, Martijn Cloos2, Florian Knoll2, and Carlos Fernandez-Granda1,3

1Courant Institute of Mathematical Sciences, NYU, New York, NY, United States, 2Center for Biomedical Imaging, NYU School of Medicine, 3Center for Data Science, NYU

This work implements a Magnetic Resonance Fingerprinting (MRF) reconstruction that accounts for the presence of multiple compartments in a voxel. We estimate the contributions of the different tissues by incorporating a sparse-recovery method, based on reweighted-l1-norm regularization, within an iterative procedure that fits a multi-compartment model to the measured k-space data. The proposed approach is validated with simulated data, as well as with a controlled phantom experiment. In addition, we present preliminary results on in-vivo measurements of a brain.

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