Regression algorithm correcting for partial volume effects in arterial spin labeling MRI

Abstract

Partial volume effects (PVE) are a consequence of limited spatial resolution in brain imaging. In arterial spin labeling (ASL) MRI, the problem is exacerbated by the nonlinear dependency of the ASL signal on magnetization contributions from each tissue within an imaged voxel. We have developed an algorithm that corrects for PVE in ASL imaging. The algorithm is based on a model that represents the voxel intensity as a weighted sum of pure tissue contribution, where the weighting coefficients are the tissue's fractional volume in the voxel. Using this algorithm, we were able to estimate cerebral blood flow (CBF) for gray matter (GM) and white matter (WM) independently. The average voxelwise ratio of GM to WM CBF was ϳ3.2, in good agreement with reports in the literature. As proof of concept, data from PVE-corrected method were compared with those from the conventional, PVE-uncorrected method. As hypothesized, the two yielded similar CBF values for voxels containing >95% GM and differed in proportion with the voxels' heterogeneity. More importantly, the GM CBF assessed with the PVEcorrected method was independent of the voxels' heterogeneity, implying that estimation of flow was unaffected by PVE. An example of application of this algorithm in motor-activation data is also given. Magn Reson Med 60:1362-1371, 2008.

Increased resting perfusion of the hippocampus in high positive schizotypy: A pseudocontinuous arterial spin labeling study

“…Finally, in response to a reviewer recommendation, we tested for a potential relationship between hippocampal CBF and gray matter volume in this region. Individual CBF values extracted from the hippocampal region showing a significant group difference were introduced as covariate in an ANCOVA design in SPM12, using gray matter segments obtained after voxel‐based morphometry preprocessing as reported in a recent study by our group in this sample (Modinos et al, 2017a), following the proposal that GMV could represent a potential confounder in ASL studies (Asllani, Borogovac, & Brown, 2008; Chappell et al, 2011). This analysis yielded no significant effects within or between groups either at p  < .05 FWE or at a more lenient threshold of p  < .001 uncorrected.…”

Mapping Brain Function Using a 30-Day Interval between Baseline and Activation: A Novel Arterial Spin Labeling fMRI Approach

“…Each subject's MPRAGE was segmented (Ashburner and Friston, 2005) to obtain voxelwise GM, white matter (WM), and cerebrospinal fluid (CSF) content images as posterior probability maps, P GM , P WM , and P CSF , respectively (Asllani et al, 2008a).…”

“…(1) For each LD value, GM ASL fractional signal images (computed as percent change between control and label images, were obtained using the PVEc algorithm as detailed in Asllani et al (2008a). A regression kernel size = 5 Â 5 Â 1 voxels was applied.…”

“…Tissue transit time (TTT) was modeled to vary linearly with ATT with an additional exchange time assumed to be 500 and 700 ms for GM and WM, respectively (Asllani et al, 2008a).…”

“…At each voxel, PVE-corrected net CBF images were computed as: CBF net = ((P GM Á CBFd GM ) + (P WM Á CBFd WM )), where P GM and P WM represent the fractional tissue content of GM and WM in a given voxel, respectively (Asllani et al, 2008a).…”