MR Fingerprinting with a Deep Image Prior Reconstruction for Combined T1, T2, and M0 Mapping and Multi-Contrast Cine Imaging

This work introduces a self-supervised deep learning reconstruction for cine Magnetic Resonance Fingerprinting, allowing for simultaneous cardiac phase-resolved T1, T2, and M0 mapping (without motion correction or averaging of data across different phases) and bright-blood and dark-blood cine imaging during a 10-second breathhold, with a temporal resolution (24 phases) comparable to standard cine imaging. Results are presented in simulations using the XCAT phantom and in healthy subjects, where the proposed reconstruction yielded reduced noise, undersampling artifacts, and motion blurring compared to previous low-rank and motion-corrected methods.