Koch, TimoTimoKochFlemisch, BerndBerndFlemischHelmig, RainerRainerHelmigWiest, Roland Gerhard RudiRoland Gerhard RudiWiestObrist, DominikDominikObrist0000-0002-6062-90762024-10-282024-10-282020-02https://boris-portal.unibe.ch/handle/20.500.12422/185278We 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 sub-voxel 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 to 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. This article is protected by copyright. All rights reserved.enNMR signal brain tissue perfusion embedded mixed-dimension microcirculation modeling multiple sclerosis600 - Technology::610 - Medicine & healtharticle10.7892/boris.1378373188331610.1002/cnm.3298