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Abstract #3750

Optimized dimensionality reduction for parameter estimation in MR fingerprinting via deep learning

Quentin Duchemin1, Kangning Liu1, Carlos Fernandez-Granda2, and Jakob Assländer3
1Center for Data Science, NYU, New York, NY, United States, 2Courant Institute of Mathematical Sciences and Center for Data Science, New York University, New York, NY, United States, 3Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, NY, United States

We propose a deep learning approach for MR fingerprinting that jointly learns a low-dimensional representation of the fingerprints and estimates biophysical parameters from this subspace. In contrast to SVD-based projections, which are agnostic to the estimation task, the learned subspace is optimized to maximize information content about the parameters of interest. Incorporating the learned basis functions in the forward imaging operator suppresses undersampling artifacts and increases computational efficiency.

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