Magnetic resonance fingerprinting based on realistic vasculature in mice

Abstract

Magnetic resonance fingerprinting (MRF) was recently proposed as a novel strategy for MR data acquisition and analysis. A variant of MRF called vascular MRF (vMRF) followed, that extracted maps of three parameters of physiological importance: cerebral oxygen saturation (SatO₂), mean vessel radius and cerebral blood volume (CBV). However, this estimation was based on idealized 2-dimensional simulations of vascular networks using random cylinders and the empirical Bloch equations convolved with a diffusion kernel. Here we focus on studying the vascular MR fingerprint using real mouse angiograms and physiological values as the substrate for the MR simulations. The MR signal is calculated ab initio with a Monte Carlo approximation, by tracking the accumulated phase from a large number of protons diffusing within the angiogram. We first study the identifiability of parameters in simulations, showing that parameters are fully estimable at realistically high signal-to-noise ratios (SNR) when the same angiogram is used for dictionary generation and parameter estimation, but that large biases in the estimates persist when the angiograms are different. Despite these biases, simulations show that differences in parameters remain estimable. We then applied this methodology to data acquired using the GESFIDE sequence with SPIONs injected into 9 young wild type and 9 old atherosclerotic mice. Both the pre injection signal and the ratio of post-to-pre injection signals were modeled, using 5-dimensional dictionaries. The vMRF methodology extracted significant differences in SatO₂, mean vessel radius and CBV between the two groups, consistent across brain regions and dictionaries. Further validation work is essential before vMRF can gain wider application.

Keywords: Ab initio Monte Carlo simulations of magnetic resonance signal; Gradient-echo sampling of free induction decay and echo (GESFIDE); Mouse model of atherosclerosis; Superparamagnetic iron oxide nanoparticle (SPION); Vascular magnetic resonance fingerprinting (vMRF).

Fig. 1

Illustration of vMRF methodology. A. Representation of a realistic angiogram, used to generate by deformations a dictionary of MR signals. B. Sample modeling of cortical mouse vMRF data (black line) to the best matching entry in the dictionary (thick red line), with the thin red lines representing 3 out of the 175 thousand other dictionary entries. From this model, simulated parameters which generated that entry are extracted, e.g. for this match: SatO₂ = 52.7%, mean vessel radius = 7.4 μm, CBV = 8.0%, SPION concentration = 3.0 and ΔB₀ = 0.0015.

Fig. 2

A. Schematic of the GESFIDE sequence. B. Sample mouse GESFIDE data, at the spin echo (echo #24) with SNR ~ 50. C. Imaging protocol.

Fig. 3

Non-estimability of parameters with a different angiogram (angiogram 4) used for dictionary generation (D0₄) and for the simulated noisy signal (angiogram 5) to be matched to the dictionary (D0₄). A bias in the estimated parameters remains even at high SNR, especially for the radius.

Fig. 4

Estimability of differences in parameters. As with Fig. 3, angiogram 4 was used for the dictionary D0₄ while the simulated noisy signal S_a was based on a different angiogram (#5). The set of all pairs (S_a,S_b) of simulated signals with different underlying parameters a and b was considered. For each pair, the double difference was calculated by subtracting the difference between the extracted and simulated parameters for parameters a from the same quantity calculated for parameters b.

Fig. 5

A. 2-sample t-tests SPMs for WT > ATX for the 5 parameters, on the vMRF maps obtained either using the “best” matching entries, the average “Avg” of the vMRF extracted values, or one of the dictionaries (#5), for the same slice as Suppl. Figs. 11 and 12. B. For the “best” matching entries, the vMRF parameter averages for WT and ATX mice, and their standard deviations. 2-sample t-tests SPMs for WT > ATX are also shown for the steady state values ss_R and ss_CBV.

Fig. 6

Results of paired t-tests for 4 regions of interest for the 3 physiological parameters. SatO₂ and Radius were significantly higher in white matter (corpus callosum and thalamus) for the mean of the extracted parameters. Blue bars (darker when printed in grayscale): WT mice; red bars: ATX mice. *: p < 0.05 Bonferroni corrected.