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.

Magnetic resonance fingerprinting – An overview

“…The T1 and T2 values obtained with MRF studies have shown good correlation with conventional T1 and T2 mapping methods and those published in literature[14,16,44–46]. The utility of MRF has also been demonstrated in preclinical and animal studies[12,21]. Clinical applications of MRF till date have been greatly focused on brain and prostate and shown promise in abdomen, musculoskeletal and cardiac applications.…”

“…Each of these steps is discussed in more detail below. While the original MRF description focused on measuring T1, T2, static magnetic field (B 0 ) inhomogeneity or off-resonance frequency and proton density M 0 [2], recent work has shown the feasibility to measure other properties such as radio frequency (RF) transmit field inhomogeneity (B 1 )[6,7], T2*[8], perfusion[9] and microvascular properties[10–12]. …”

“…R and SO 2 maps were more homogenous[10](Figure 6). Other authors have further built upon this foundation and applied MRvF in rats studies to either get more realistic vascular maps[12] or evaluate differences in microvascular properties in strokes and brain tumors[11]. Lemasson, et al [11]tested the MRvF technique in 115 rats divided into three models, namely brain tumors, strokes and healthy animals, to obtain maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously.…”

Flow MR fingerprinting

“…It has been demonstrated that the transient‐state signal can provide quantitative information about T 1 , T 2 , B 0 , B 1 + , and other parameters depending on the applied MRF pattern (i.e., the excitation, gradients, and timing pattern). Besides these parameters, attempts to quantify physiological parameters such as diffusion or tissue vascularization have been pursued …”

“…Besides these parameters, attempts to quantify physiological parameters such as diffusion or tissue vascularization have been pursued. 5,6 Another physiological parameter of rising interest and with strong potential for MRF is the blood flow velocity. 7 Time-resolved quantification of flow velocities is used in clinical routine for investigating cardiovascular diseases.…”