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• W1977221251 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 ± 2.4 μm, 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." @default.
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• W1977221251 date "2014-04-01" @default.
• W1977221251 modified "2023-10-17" @default.
• W1977221251 title "MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain" @default.
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• W1977221251 doi "https://doi.org/10.1016/j.neuroimage.2013.11.052" @default.
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