Imaging and Enhancing Cerebrospinal Fluid Flow with In silico, In vitro and In vivo Studies

The roles of cerebrospinal fluid (CSF) are diverse and include cushioning the brain, regulating intracranial pressure, and clearing metabolic waste. Disruptions in CSF flow contribute to pathologies ranging from hydrocephalus to Alzheimer’s disease. Here, we study CSF flow using in silico computational fluid dynamic (CFD) based models, in vitro 4D Flow MRI of 3D printed models, and in vivo 4D Flow MRI in healthy individuals. Study of CSF flow using 4D Flow MRI has been limited by low spatiotemporal resolution and velocity-to-noise ratios. We examine the feasibility of physics-guided neural networks (PGNN) in super-resolving and denoising 4D Flow MRI within the cerebral ventricles. We show PGNNs can reconstruct dominant flow structures such as counter-rotating vortices in the 3rd ventricle from CFD based synthetic 4D Flow MRI. By incorporating divergence-based regularization in our loss function, the RMSE of near-wall velocities was reduced by 15%. We assess reconstruction accuracy by imaging 1-to-1 and scaled 2-to-1 phantoms while conserving voxel size and matching Reynolds and Womersley numbers in the larger phantom. Using PGNN may enable increased use of 4D Flow MRI for analysis of CSF flow dynamics in cerebroventricular and neurodegenerative disorders.