Estimation of cerebral blood flow from dynamic susceptibility contrast MRI using a tissue model

Cerebral perfusion measurements are of great clinical and research interest. Positron emission tomography (PET) is considered the gold standard for cerebral perfusion measurement, but is not widely available and entails exposure of the subject to radioactivity. Dynamic susceptibility contrast (DSC) MRI methods are becoming more widely available on the newest generation of MRI scanners. The standard analysis methods of this data have significant disadvantages that include the use of a single, difficult to measure, arterial input function for the entire brain and the need to perform a numerical deconvolution on the logarithm of noisy data. These methods are not yet fully validated and remain qualitative in nature. Using a modification of the standard tracer kinetic principles we implemented a tissue perfusion model that has several advantages over standard methods. The model parameters were estimated using Bayes probability theory in a group of patients with varying degrees of hemodynamic impairment and were found to provide additional physiologic information that was not available using standard techniques.