Joint Estimation of AIF and Perfusion Parameters from Dynamic Susceptibility Contrast MRI in Mouse Gliomas Using a Tissue Model

Kathleen E. Chaffee¹, Joshua S. Shimony¹, G. Larry Bretthorst¹, Joel R. Garbow¹

DSC MRI provides valuable perfusion parameters that correlate with brain tumor progression, but requires a difficult to measure arterial input function (AIF). Using a modification of standard tracer kinetics applied to a tissue perfusion model allows both the AIF and residue curve to be determined for each pixel. The parameters are estimated by Bayesian probability theory using Markov chain Monte Carlo simulations to sample the joint posterior probabilities for the parameters. Here we report DSC MRI investigations on a mouse gliomas that demonstrates characteristic perfusion parameters that do not require an independent measurement of an AIF.