• Kevin M Tangen, Ying Hsu, David C Zhu, and Andreas A Linninger.
• Laboratory for Product and Process Design, Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan Street-218, Chicago, IL 60607, USA.
• J Biomech. 2015 Jul 16; 48 (10): 2144-54.

AbstractSpinal microstructures are known to substantially affect cerebrospinal fluid patterns, yet their actual impact on flow resistance has not been quantified. Because the length scale of microanatomical aspects is below medical image resolution, their effect on flow is difficult to observe experimentally. Using a computational fluid mechanics approach, we were able to quantify the contribution of micro-anatomical aspects on cerebrospinal fluid (CSF) flow patterns and flow resistance within the entire central nervous system (CNS). Cranial and spinal CSF filled compartments were reconstructed from human imaging data; microscopic trabeculae below the image detection threshold were added artificially. Nerve roots and trabeculae were found to induce regions of microcirculation, whose location, size and vorticity along the spine were characterized. Our CFD simulations based on volumetric flow rates acquired with Cine Phase Contrast MRI in a normal human subject suggest a 2-2.5 fold increase in pressure drop mainly due to arachnoid trabeculae. The timing and phase lag of the CSF pressure and velocity waves along the spinal canal were also computed, and a complete spatio-temporal map encoding CSF volumetric flow rates and pressure was created. Micro-anatomy induced fluid patterns were found responsible for the rapid caudo-cranial spread of an intrathecally administered drug. The speed of rostral drug dispersion is drastically accelerated through pulsatile flow around microanatomy induced vortices. Exploring massive parallelization on a supercomputer, the feasibility of computational drug transport studies was demonstrated. CNS-wide simulations of intrathecal drugs administration can become a practical tool for in silico design, interspecies scaling and optimization of experimental drug trials. Copyright © 2015 Elsevier Ltd. All rights reserved.

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