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Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
Published on: May 7, 2019
Computational model of interstitial transport in the spinal cord using diffusion tensor imaging
Malisa Sarntinoranont1, Xiaoming Chen, Jianbing Zhao
1Department of Mechanical and Aerospace Engineering, University of Florida, 212 MAE-A, PO Box 116250, Gainesville, FL, 32611-6250, USA. sarntm@ufl.edu
Annals of Biomedical Engineering
|July 13, 2006
Summary
This study introduces a 3D modeling method using diffusion tensor imaging (DTI) to predict drug distribution in nervous tissue after convection-enhanced delivery (CED). The model accurately predicts drug spread, aiding in optimizing local drug delivery strategies.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Computational Biology
Background:
- Local drug delivery, such as convection-enhanced delivery (CED), aims to improve drug distribution in specific nervous tissue regions.
- Accurate 3D models are needed to predict the spatial spread of drugs within complex neural tissues.
- Existing methods lack the resolution to fully capture the anisotropic transport properties of neural tissue.
Purpose of the Study:
- To develop and validate a 3D modeling methodology for predicting macromolecule distribution in nervous tissue using diffusion tensor imaging (DTI).
- To incorporate directional transport properties derived from DTI into computational models of interstitial transport.
- To simulate convection-enhanced delivery (CED) in a rat spinal cord model and compare predictions with experimental data.
Main Methods:
- Processing of magnetic resonance microscopy (MRM) and DTI scans to segment gray and white matter.
- Deriving fiber tract orientation from DTI to define anisotropic hydraulic conductivity (K) and tracer diffusivity (Dt) tensors.
- Solving porous media equations for interstitial fluid dynamics and albumin distribution using the finite volume method.
- Simulating CED in a rat spinal cord dorsal white matter column model.
Main Results:
- The DTI-based model successfully predicted spatial drug distribution trends for small infusion volumes (approx. 1 microl).
- Simulations showed good agreement with experimental data, though greater albumin loss was observed at larger volumes (>2 microl).
- Model predictions were comparable to simulations using fixed transport properties due to the predominantly axial white matter fiber alignment.
Conclusions:
- The developed DTI-based methodology provides a validated approach for modeling interstitial transport in nervous tissue.
- This technique can be applied to predict drug distribution in regions with complex fiber tract organization, such as the brain.
- The model aids in optimizing local drug delivery strategies by predicting drug spread and potential loss.
