Abstract
Compared to gold-standard measurements of cerebral perfusion with positron emission tomography using H(2)[(15)O] tracers, measurements with dynamic susceptibility contrast MR are more accessible, less expensive, and less invasive. However, existing methods for analyzing and interpreting data from dynamic susceptibility contrast MR have characteristic disadvantages that include sensitivity to incorrectly modeled delay and dispersion in a single, global arterial input function. We describe a model of tissue microcirculation derived from tracer kinetics that estimates for each voxel a unique, localized arterial input function. Parameters of the model were estimated using Bayesian probability theory and Markov-chain Monte Carlo, circumventing difficulties arising from numerical deconvolution. Applying the new method to imaging studies from a cohort of 14 patients with chronic, atherosclerotic, occlusive disease showed strong correlations between perfusion measured by dynamic susceptibility contrast MR with localized arterial input function and perfusion measured by quantitative positron emission tomography with H(2)[(15)O]. Regression to positron emission tomography measurements enabled conversion of dynamic susceptibility contrast MR to a physiologic scale. Regression analysis for localized arterial input function gave estimates of a scaling factor for quantitation that described perfusion accurately in patients with substantial variability in hemodynamic impairment.