Optimal Location for Arterial Input Function Measurements near the Middle Cerebral Artery in First-Pass Perfusion MRI

Abstract

One of the main difficulties in obtaining quantitative perfusion values from dynamic susceptibility contrast-magnetic resonance imaging is a correct arterial input function (AIF) measurement, as partial volume effects can lead to an erroneous shape and amplitude of the AIF. Cerebral blood flow and volume scale linearly with the area under the AIF, but shape changes of the AIF can lead to large, nonlinear errors. Current manual and automated AIF selection procedures do not guarantee the exclusion of partial volume effects from AIF measurements. This study uses a numerical model, validated by phantom experiments, for predicting the optimal location for AIF measurements in the vicinity of the middle cerebral artery (MCA). Three different sequences were investigated and evaluated on a voxel-by-voxel basis by comparison with the ground truth. Subsequently, the predictions were evaluated in an in vivo example. The findings are fourfold: AIF measurements should be performed in voxels completely outside the artery, here a linear relation should be assumed between DR Ã 2 and the concentration contrast agent, the exact optimal location differs per acquisition type, and voxels including a small MCA yield also correct AIF measurements for segmented echo planar imaging when a short echo time was used.

A Fully Automated Method for Quantitative Cerebral Hemodynamic Analysis Using DSC–MRI

“…The large deviation in the reported normal perfusion values probably reflects both true inter-and intra-patient physiological variations (Leenders et al, 1990), as well as methodological differences between the different imaging techniques used to measure cerebral perfusion. The methodological challenges are particularly apparent in MR-based perfusion analysis and an extensive literature exists in addressing the various problems relating to AIF selection (Bleeker et al, 2009;Calamante et al, 2004;Mouridsen et al, 2006a), PVEs in the AIF (Kjolby et al, 2009;van Osch et al, 2005), non-linear dose response (Calamante et al, 2007;Kiselev, 2005), and choice of optimal deconvolution technique and model parameters (Calamante, 2005;Calamante et al, 2003;Knutsson et al, 2004;Murase et al, 2001;Wu et al, 2003). Since the aim of this study was to establish a userindependent tool for robust automated quantitative perfusion analysis, several of the most commonly used deconvolution techniques were tested and compared.…”

Effects of blood ΔR2* non-linearity on absolute perfusion quantification using DSC-MRI: Comparison with Xe-133 SPECT

“…Considering that the tissue R 2 * response to the contrast agent is linear [4], and that extravascular spins in the vicinity of large vessels show a nontrivial R 2 *-versus-concentration relationship (which under certain specific conditions can be linear) [8], it is difficult to predict whether it would be most appropriate to apply a linear or a non-linear R 2 *-versus-concentration relationship to a practical AIF measurement.…”

Reliable Perfusion Maps in Stroke MRI Using Arterial Input Functions Derived From Distal Middle Cerebral Artery Branches

“…8 Compared with large arteries, they further have the advantage that even voxels including the vessel still yield adequate AIF measurements. 12,13 Despite the influence of the corresponding slice level from which the AIF was derived, we demonstrated that there were significant differences between results of whole slice analyses and MCA branch analyses. Noise of the whole slice at the level of M1 was half of that measured at the artery, but this difference between whole slice analyses and corresponding artery decreased with higher levels (Table 1).…”