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Computational Model of Interstitial Transport in the Spinal Cord using Diffusion Tensor Imaging

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Abstract

Local drug delivery methods, including convection-enhanced delivery (CED), are being used to increase distribution in selected regions of nervous tissue. There is a need for 3D models that predict spatial drug distribution within these tissues. A methodology was developed to process magnetic resonance microscopy (MRM) and diffusion tensor imaging (DTI) scans, segment gray and white matter regions, assign tissue transport properties, and model the interstitial transport of macromolecules. Fiber tract orientation was derived from DTI data and used to assign directional dependence of hydraulic conductivity, K, and tracer diffusivity, D t , transport tensors. Porous media solutions for interstitial fluid pressure, velocity, and albumin distribution were solved using a finite volume method. To test this DTI-based methodology, a rat spinal cord transport model was developed to simulate CED into the dorsal white matter column. Predicted distribution results correspond well with small volume (∼1 μl) trends found experimentally, although albumin loss was greater at larger infusion volumes (>2 μl). Simulations were similar to those using fixed transport properties due to the bulk alignment of white matter fibers along the cord axis. These findings help to validate the DTI-based methodology which can be applied to modeling regions where fiber tract organization is more complex, e.g., the brain.

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Abbreviations

CED:

convection-enhanced delivery

MRM:

magnetic resonance microscopy

DTI:

diffusion tensor imaging

FEM:

finite element method

PBS:

phosphate buffered saline

DWI:

diffusion-weighted imaging

NURBS:

non-uniform rational B-spline surfaces

CSF:

cerebrospinal fluid

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ACKNOWLEDGMENTS

We would like to thank Sara Berens for assistance with MRI data collection and Dr. Robert Yezierski for providing the fixed, excised rat spinal cord sample. The authors would also like to thank Dr. Peter Basser for helpful discussions on incorporating DTI into predictive models, and Dr. Russell Lonser for consultation on the CED technique. The MRI data was obtained at the Advanced Magnetic Resonance Imaging and Spectroscopy Facility in the McKnight Brain Institute and National High Magnetic Field Laboratory of the University of Florida. The work was supported in part by the National Institutes of Health through grant P41 RR16105 (THM).

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Florida, 212 MAE-A, PO Box 116250, Gainesville, FL, 32611-6250, USA

    Malisa Sarntinoranont, Xiaoming Chen & Jianbing Zhao

  2. Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, 32611, USA

    Thomas H. Mareci

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  1. Malisa Sarntinoranont
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  2. Xiaoming Chen
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Correspondence to Malisa Sarntinoranont.

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Sarntinoranont, M., Chen, X., Zhao, J. et al. Computational Model of Interstitial Transport in the Spinal Cord using Diffusion Tensor Imaging. Ann Biomed Eng 34, 1304–1321 (2006). https://doi.org/10.1007/s10439-006-9135-3

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