A Statistical Imaging Framework for Magnetic Resonance Fingerprinting: Optimized Encoding and Decoding

MRI scans are primarily performed and evaluated in a qualitative way using contrast-weighted images (e.g., with T1, T2 or proton-density weighting). These images weighting is a complex function of one or more of these intrinsic MR tissue parameters as modulated by external scanner settings and imperfections, providing limited capability for direct inter- and intra- patient comparisons across different institutions and/or across scanners. Although the potentials of quantitative MRI, which directly maps the underlying MR tissue parameters, have long been recognized, achieving this goal often requires lengthy acquisition times. Magnetic resonance fingerprinting (MRF) is a recent breakthrough in rapid quantitative MRI, which features a random or pseudo-random excitation scheme for encoding, as well as a dictionary matching scheme for decoding.
Despite a brand new concept, the encoding and decoding processes for MRF are heuristic. In this talk, I will present our recent research that introduces a novel and rigorous statistical framework for optimized MRF. On the decoding side, I will introduce a principled statistical reconstruction approach, and show that the conventional MRF reconstruction is equivalent to the first iteration of the maximum likelihood reconstruction. On the encoding side, I will characterize the acquisition efficiency of MRF using estimation-theoretic bounds, and further use these bounds as metrics to perform optimal experimental design (e.g., designing flip angle and repetition time schedule). Surprisingly, the optimized acquisition parameters appear to be highly structured rather than randomly varying. In the end, I will make the connection between the optimal experiment design and optimal control theory, and discuss the structured behavior of optimized acquisition parameters.