Patient-specific computational fluid dynamic simulation of cerebrospinal fluid flow in the intracranial space

Abnormal cerebrospinal fluid (CSF) flow is associated with a variety of poorly understood neurological disorders such as Alzheimer's Disease and hydrocephalus. The lack of comprehensive understanding of the fluid and solid mechanics of CSF flow remains a critical barrier in the development of diagnostic assessment and potential treatment options for these diseases. We have developed a whole brain, patient-specific computational fluid dynamics (CFD) simulation of CSF flow in the cranial cavity as a step towards comprehensive understanding of CSF dynamics and how they relate to neurodegenerative diseases.

A patient-specific 3D geometry of the CSF filled spaces was segmented from structural MRI. Patient-specific boundary conditions were measured using phase contrast MRI. A rigid wall three-dimensional CFD simulation was conducted using only patient-specific waveforms as boundary conditions. Deformation of brain tissue is accounted for using volumetric flowrate boundary conditions calculated via the conservation of mass. Phase contrast MRI measurement of maximum velocity at the cerebral aqueduct was used to validate the simulation with excellent agreement.

The CSF dynamics across the cardiac cycle are presented, illustrating the relationship between arterial flow and CSF flow. Flow in and out of the ventricles was shown to have a slight phase delay (∼20 % of the cardiac cycle) from flow in the subarachnoid space. Intracranial pressure dynamics are presented, with pressure in the Lateral Ventricles demonstrating less significant transient effects than pressure in the subarachnoid space.

This work presents a quantitatively validated whole-brain simulation of CSF flow for a single healthy subject. The computational methodology improves over the state of the art by eliminating non-physiological boundary conditions and unnecessary assumptions about the mechanical properties of brain tissue, providing an essential step towards clinically useful tools for assessing the development of neurodegenerative disorders.