A multiscale subvoxel perfusion model to estimate diffusive capillary wall conductivity in multiple sclerosis lesions from perfusion MRI data. (27th January 2020) - Koch, Timo
Abstract: We propose a new mathematical model to learn capillary leakage coefficients from dynamic susceptibility contrast MRI data. To this end, we derive an embedded mixed‐dimension flow and transport model for brain tissue perfusion on a subvoxel scale. This model is used to obtain the contrast agent concentration distribution in a single MRI voxel during a perfusion MRI sequence. We further present a magnetic resonance signal model for the considered sequence including a model for local susceptibility effects. This allows modeling MR signal‐time curves that can be compared with clinical MRI data. The proposed model can be used as a forward model in the inverse modeling problem of inferring model parameters such as the diffusive capillary wall conductivity. Acute multiple sclerosis lesions are associated with a breach in the integrity of the blood‐brain barrier. Applying the model to perfusion MR data of a patient with acute multiple sclerosis lesions, we conclude that diffusive capillary wall conductivity is a good indicator for characterizing activity of lesions, even if other patient‐specific model parameters are not well‐known. Abstract : A subvoxel perfusion model is developed, which allows to simulate the contrast agent distribution dynamics in a small brain tissue sample, discretely resolving the embedded microvasculature as a tubular network. An MRI model developed on the same scale allows to compare the simulation results with clinical MRI voxel data obtained with a DSC‐MRI sequence from patients suffering from multiple sclerosis. Using inverse modeling techniques, contrast agent leakage and the capillary wall permeability in lesions can be quantified under consideration of uncertainty.