Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. In this abstract we investigate and evaluate advantages of a deep learning approach for embedding the manifold of solutions of the Bloch equations and to address these shortcomings.
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