A flexible MRF approach to improve kinetic rate estimation with bSSFP-based hyperpolarized [1-13C]pyruvate MRI.

Abstract

Purpose: In this work, we adopt the MR fingerprinting (MRF) framework and leverage its flexibility in quantitative pulse sequence design to propose improved balanced steady-state free precession (bSSFP)-based hyperpolarized Carbon-13 (¹³C) acquisitions for robust metabolic conversion rate quantification.

Methods: Spectrally selective bSSFP-based acquisitions with variable RF excitation were implemented for [1-¹³C]pyruvate and used in conjunction with prior implementation of [1-¹³C]lactate selective bSSFP imaging. MRF framework parameter estimation was performed using dictionary-based template matching. Influences of bSSFP-based acquisitions and sigmoid RF excitation scheme were assessed with simulation experiments and Monte Carlo evaluation. Methods were then compared using experimental data from rat kidney acquired on a clinical 3 T scanner.

Results: Simulations indicated that combining bSSFP-based acquisitions and variable RF excitation (MRF-Sigmoid) exhibited bias <0.1% across the majority (86%) of combinations of pyruvate-to-lactate conversion rate (k_PL) and noise level investigated when estimating k_PL with the MRF framework. bSSFP-based experiments, with and without sigmoid excitation scheme, showed lower variance in fits at all levels of k_PL and noise investigated compared to the method used in prior work by this group (hybrid gradient echo). Positive, linear correlations were found for in vivo voxel-wise estimates of k_PL in healthy rat kidneys when comparing all experiment methods. MRF-Sigmoid experiment design increased pyruvate cumulative SNR by 3.5-fold over hybrid gradient echo while maintaining similar lactate cumulative SNR.

Conclusion: The use of the MRF framework for k_PL estimation demonstrates the feasibility of dictionary-based template matching and can be used to accurately estimate physiologically relevant k_PL and improve cumulative SNR.