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Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery arebottlenecked by the heavy storage and computation requirements of adictionary-matching (DM) step due to the growing size and complexity of thefingerprint dictionaries in multi-parametric quantitative MRI applications. Inthis paper we study a deep learning approach to address these shortcomings.Coupled with a dimensionality reduction first layer, the proposed MRF-Net isable to reconstruct quantitative maps by saving more than 60 times in memoryand computations required for a DM baseline. Fine-grid manifold enumerationi.e. the MRF dictionary is only used for training the network and not duringimage reconstruction. We show that the MRF-Net provides a piece-wise affineapproximation to the Bloch response manifold projection and that rather thanmemorizing the dictionary, the network efficiently clusters this manifold andlearns a set of hierarchical matched-filters for affine regression of the NMRcharacteristics in each segment.

Geometry of Deep Learning for Magnetic Resonance Fingerprinting