MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain

Abstract

In the present study, we describe a fingerprinting approach to analyze the time evolution of the MR signal and retrieve quantitative information about the microvascular network. We used a Gradient Echo Sampling of the Free Induction Decay and Spin Echo (GESFIDE) sequence and defined a fingerprint as the ratio of signals acquired pre- and post-injection of an iron-based contrast agent. We then simulated the same experiment with an advanced numerical tool that takes a virtual voxel containing blood vessels as input, then computes microscopic magnetic fields and water diffusion effects, and eventually derives the expected MR signal evolution. The parameter inputs of the simulations (cerebral blood volume [CBV], mean vessel radius [R], and blood oxygen saturation [SO2]) were varied to obtain a dictionary of all possible signal evolutions. The best fit between the observed fingerprint and the dictionary was then determined by using least square minimization. This approach was evaluated in 5 normal subjects and the results were compared to those obtained by using more conventional MR methods, steady-state contrast imaging for CBV and R and a global measure of oxygenation obtained from the superior sagittal sinus for SO2. The fingerprinting method enabled the creation of high-resolution parametric maps of the microvascular network showing expected contrast and fine details. Numerical values in gray matter (CBV=3.1±0.7%, R=12.6±2.4μm, SO2=59.5±4.7%) are consistent with literature reports and correlated with conventional MR approaches. SO2 values in white matter (53.0±4.0%) were slightly lower than expected. Numerous improvements can easily be made and the method should be useful to study brain pathologies.

Keywords: Blood oxygen saturation; Cerebral blood volume; Fingerprint; Magnetic resonance imaging; Numerical simulation; Vessel size imaging.

Figure 1

Summary of MR vascular fingerprinting. (a) A numerical simulation with different parameters for CBV, vessel size (R), and oxygen saturation (SO2) is used to create a family of curves (the dictionary) (b). (c,d) The actual fingerprint derived from GESFIDE imaging is then compared to this dictionary to find the underlying parameters that make the best match.

Figure 2

Root mean square deviation plots illustrating the dissimilarity between a simulated reference MR signal (indicated by the white arrow) and the other simulated MR signals of the dictionary. Two dictionaries are investigated: The dictionary prior to the contrast agent injection (a-c) and the dictionary of the vascular fingerprint generated by the ratio Post/Pre contrast agent injection (d-f). Three orthogonal planes that intersect at the reference MR signal are represented. Large dispersions of minima (undesirable) are observed in the dictionary prior to contrast injection (a-c) whereas more localized minima (desirable) are noticed in the pre- and post-contrast ratio based vascular fingerprint dictionary (d-f). Note the non-linear scales.

Figure 3

Box-and-whisker plots illustrating the reliability of the vascular fingerprinting approach. Estimated R, CBV, and SO2 values are compared to their theoretical values. Linear trends are observed for all parameters. A lower accuracy is noticed in the SO2 estimates and to some extent in the estimates of radii compared to the CBV estimates (mean absolute relative error for SO2=21%, R=9%, and CBV=4%).

Figure 4

a) GESFIDE images at TE=4.9ms and TE=25.6ms (GE) and at TE=100ms (SE) prior and after the contrast agent injection. b) Normalized vascular fingerprints averaged over the whole acquisition gray matter (square) and the white matter (triangles) volume. c) Example of the fingerprint for one voxel (dots) and its corresponding match from the dictionary (line).

Figure 5

Representative parametric maps obtained in volunteer #4 (who had the highest correlation coefficient for the match between the fingerprint and the dictionary). Voxel with r² < 0.5 are discarded. White and blue arrows indicate that the technique is sensitive enough to detect the relatively small medullary veins of the deep white matter.

Figure 6

Relationship between vascular measurements from the different methods evaluated from regions of interest in the whole white matter (squares) or grey matter (triangles) volumes in the normal subjects (n=5) and in each of the 5 slices. Each volunteer is identified by a different color. In (a-c), the equations of linear relations are derived from the fixed effect estimations. The level of significance is also listed. a) Relationship between ΔR2* (1/s) and CBV; b) steady-state CBV (%) and CBV; c) and ΔR2*/ΔR2 and R. d) Linear regression between global sagittal sinus SvO2 and SO2 in grey matter.