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

## TL;DR

This paper introduces MRF-X, an extension of MRF MRI, to recover six tissue properties simultaneously, but finds that recovering all six remains challenging.

## Contribution

The paper introduces MRF-X and evaluates optimization techniques for recovering multiple tissue properties from MRF-X data.

## Key findings

- MRF-X can recover five tissue properties successfully using SHGO and L-BFGS-B optimization.
- Recovering all six tissue properties remains challenging with current methods and pulse sequences.

## 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 T1a,T2a, T1b, T2b, ρ and τ simultaneously. In MRF-X, ‘a’ and ‘b’ refer to distinct compartments modeled in each voxel, while ρ is the fractional volume of component ‘a’, and τ 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.