Imperial College London
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ProfessorDanieleDini

Faculty of Engineering, Department of Mechanical Engineering

Professor in Tribology
 
 
 
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Contact

 

+44 (0)20 7594 7242d.dini Website

 
 
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Location

 

669City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Vidotto:2021:10.1007/s10439-020-02598-7,
author = {Vidotto, M and Pederzani, M and Castellano, A and Pieri, V and Falini, A and Dini, D and De, Momi E},
doi = {10.1007/s10439-020-02598-7},
journal = {Annals of Biomedical Engineering},
pages = {689--702},
title = {Integrating diffusion tensor imaging and neurite orientation dispersion and density imaging to improve the predictive capabilities of CED models},
url = {http://dx.doi.org/10.1007/s10439-020-02598-7},
volume = {49},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper aims to develop a comprehensive and subject-specific model to predict the drug reach in Convection-Enhanced Delivery (CED) interventions. To this end, we make use of an advance diffusion imaging technique, namely the Neurite Orientation Dispersion and Density Imaging (NODDI), to incorporate a more precise description of the brain microstructure into predictive computational models. The NODDI dataset is used to obtain a voxel-based quantification of the extracellular space volume fraction that we relate to the white matter (WM) permeability. Since the WM can be considered as a transversally isotropic porous medium, two equations, respectively for permeability parallel and perpendicular to the axons, are derived from a numerical analysis on a simplified geometrical model that reproduces flow through fibre bundles. This is followed by the simulation of the injection of a drug in a WM area of the brain and direct comparison of the outcomes of our results with a state-of-the-art model, which uses conventional diffusion tensor imaging. We demonstrate the relevance of the work by showing the impact of our newly derived permeability tensor on the predicted drug distribution, which differs significantly from the alternative model in terms of distribution shape, concentration profile and infusion linear penetration length.
AU  - Vidotto,M
AU  - Pederzani,M
AU  - Castellano,A
AU  - Pieri,V
AU  - Falini,A
AU  - Dini,D
AU  - De,Momi E
DO  - 10.1007/s10439-020-02598-7
EP  - 702
PY  - 2021///
SN  - 0090-6964
SP  - 689
TI  - Integrating diffusion tensor imaging and neurite orientation dispersion and density imaging to improve the predictive capabilities of CED models
T2  - Annals of Biomedical Engineering
UR  - http://dx.doi.org/10.1007/s10439-020-02598-7
UR  - http://hdl.handle.net/10044/1/82329
VL  - 49
ER  -
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