Detecting focal changes in CBF independently from tissue content using arterial spin labeling (ASL) fMRI

shape of the regression kernel based on its overlap with hypothesized activation ROI (right). As the regression kernel is positioned at various locations throughout the image, the algorithm checks whether it also overlaps with the activation kernel (purple). If there is no overlap, all voxels within the kernel contribute to linear regression as per the standard PVEc method. Otherwise, the algorithm checks whether the overlap affects the center voxel. If the center voxel is affected, the linear regression includes only the voxels which are also within the activation kernel. Conversely, if the center voxel is not within the activation kernel, the linear regression excludes all voxels within the activation ROI.