Recovery and Characterization of Tissue Properties from Magnetic Resonance Fingerprinting with Exchange

Abstract

Magnetic resonance fingerprinting (MRF), a quantitative MRI technique, enables the acquisition of multiple tissue properties in a single scan. In this paper, we study a proposed extension of MRF, MRF with exchange (MRF-X), which can enable acquisition of the six tissue properties $T_1^a$,$T_2^a$, $T_1^b$, $T_2^b$, $\rho$ and $\tau$ simultaneously. In MRF-X, ‘a’ and ‘b’ refer to distinct compartments modeled in each voxel, while $\rho$ is the fractional volume of component ‘a’, and $\tau$ is the exchange rate of protons between the two components. To assess the feasibility of recovering these properties, we first empirically characterize a similarity metric between MRF and MRF-X reconstructed tissue property values and known reference property values for candidate signals. Our characterization indicates that such a recovery is possible, although the similarity metric surface across the candidate tissue properties is less structured for MRF-X than for MRF. We then investigate the application of different optimization techniques to recover tissue properties from noisy MRF and MRF-X data. Previous work has widely utilized template dictionary-based approaches in the context of MRF; however, such approaches are infeasible with MRF-X. Our results show that Simplicial Homology Global Optimization (SHGO), a global optimization algorithm, and Limited-memory Bryoden–Fletcher–Goldfarb–Shanno algorithm with Bounds (L-BFGS-B), a local optimization algorithm, performed comparably with direct matching in two-tissue property MRF at an SNR of 5. These optimization methods also successfully recovered five tissue properties from MRF-X data. However, with the current pulse sequence and reconstruction approach, recovering all six tissue properties remains challenging for all the methods investigated.