Reliable estimation of capillary transit time distributions at voxel-level using DSC-MRI

Introduction DSC-MRI has proven very useful for characterizing tissue perfusion indices such as CBF, CBV and MTT. However, since CBF and MTT only represent average capillary flow and transit time, a more exhaustive characterization of local capillary flow patterns could be achieved by estimating the entire underlying transit time distribution. Flow heterogeneity in particular, or analogously transit time heterogeneity (TTH), has been suggested as an important physiological parameter correlating with outcome in acute ischemic stroke [1], and tissue oxygen extraction capacity (OEC) has been suggested to depend on both heterogeneity and MTT [2]. Estimation of these physiological parameters is contingent on accurate identification of the shape of the residue function. In this study we adopt a parametric, Bayesian model for the residue function and explore the feasibility of simultaneously estimating mean transit time and the shape related quantities OEC and TTH. Materials and methods Simulation. We first generate concentration curves using the standard tracer kinetic principle relating tracer concentration to convolution between the arterial input function (AIF) and residue function. We use a gamma density function to characterize the distribution of trans-capillary transit times, which has previously been used to represent a range of microvascular flow patterns [3]: