• Andreas A Linninger, Michalis Xenos, David C Zhu, MahadevaBharath R Somayaji, Srinivasa Kondapalli, and Richard D Penn.
• Laboratory for Product and Process Design (LPPD), Department of Chemical and Bioengineering, University of Illinois at Chicago 60607, USA. linninge@uic.edu
• IEEE Trans Biomed Eng. 2007 Feb 1; 54 (2): 291-302.

AbstractAdvances in magnetic resonance (MR) imaging techniques enable the accurate measurements of cerebrospinal fluid (CSF) flow in the human brain. In addition, image reconstruction tools facilitate the collection of patient-specific brain geometry data such as the exact dimensions of the ventricular and subarachnoidal spaces (SAS) as well as the computer-aided reconstruction of the CSF-filled spaces. The solution of the conservation of CSF mass and momentum balances over a finite computational mesh obtained from the MR images predict the patients' CSF flow and pressure field. Advanced image reconstruction tools used in conjunction with first principles of fluid mechanics allow an accurate verification of the CSF flow patters for individual patients. This paper presents a detailed analysis of pulsatile CSF flow and pressure dynamics in a normal and hydrocephalic patient. Experimental CSF flow measurements and computational results of flow and pressure fields in the ventricular system, the SAS and brain parenchyma are presented. The pulsating CSF motion is explored in normal and pathological conditions of communicating hydrocephalus. This paper predicts small transmantle pressure differences between lateral ventricles and SASs (approximately 10 Pa). The transmantle pressure between ventricles and SAS remains small even in the hydrocephalic patient (approximately 30 Pa), but the ICP pulsatility increases by a factor of four. The computational fluid dynamics (CFD) results of the predicted CSF flow velocities are in good agreement with Cine MRI measurements. Differences between the predicted and observed CSF flow velocities in the prepontine area point towards complex brain-CSF interactions. The paper presents the complete computational model to predict the pulsatile CSF flow in the cranial cavity.

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