Evaluating amplified magnetic resonance imaging as an input for computational fluid dynamics models of the cerebrospinal fluid.

Computational models that accurately capture cerebrospinal fluid (CSF) dynamics are valuable tools to study neurological disorders and optimize clinical treatments. While CSF dynamics interrelate with deformations of the ventricular volumes, these deformations have been simplified and even discarded in computational models because of the lack of detailed measurements. Amplified magnetic resonance imaging (aMRI) enables visualization of these complex deformations, but this technique has not been used for predicting CSF dynamics. To assess the feasibility of using aMRI as an input for computational fluid dynamics (CFD) models of the CSF, we deduced the amplified deformations of the cerebral ventricles from an aMRI dataset and imposed these deformations in our CFD model. Then, we compared the resulting CSF flow rates with those measured in vivo. The aMRI deformations yielded CSF flow following a pulsatile pattern in line with the flow measurements. The CSF flow rates were, however, subject to noise and increased. As a result, scaling of the deformations with a factor 1/8 was necessary to match the measured flow rates. This is the first application of aMRI for modelling CSF flow, and we demonstrate that incorporating non-uniform deformations can contribute to more detailed predictions and advance our understanding of ventricular CSF dynamics.