Computer Sciences Commons^™

Quantitative Analysis Of Arterial Spin Labeling Fmri Data Using A General Linear Model, Luis Hernandez-Garcia, Hesamoddin Jahanian, Daniel Rowe

Mathematics, Statistics and Computer Science Faculty Research and Publications

Arterial spin labeling techniques can yield quantitative measures of perfusion by fitting a kinetic model to difference images (tagged-control). Because of the noisy nature of the difference images investigators typically average a large number of tagged versus control difference measurements over long periods of time. This averaging requires that the perfusion signal be at a steady state and not at the transitions between active and baseline states in order to quantitatively estimate activation induced perfusion. This can be an impediment for functional magnetic resonance imaging task experiments. In this work, we introduce a general linear model (GLM) that specifies Blood …