DAES: Self-Supervised Parameter Estimation Model for MR Fingerprinting

Motivation: Accurate estimation of relaxation parameters using MRF requires lengthy acquisitions as it benefits from having multiple spiral interleaves to boost the data quality. Goal(s): We aim to reduce acquisition time by denoising highly under-sampled data while retaining the fidelity of the estimated parameter maps. Approach: An unsupervised convolutional neural network called DAES is proposed. It combines Denoising Auto-coder (DAE) with subspace modeling, taking advantage of both denoising framework and Bloch simulation-based dictionary information. Results: DAES outperforms conventional dictionary matching in both simulated and in-vivo data for MRF, demonstrating stronger ability to estimate parameters from highly under-sampled data. Impact: Magnetic Resonance Fingerprinting with the proposed unsupervised Denoising Auto-encoder permits high-quality T1 and T2 mapping while substantially reducing the acquisition time.