Li, Z;
Chen, D;
Guo, L;
(2022)
Cerebral Perfusion of Multiple-Network Poroelastic Model by Integrating Fractional Anisotropy.
In:
ACM International Conference Proceeding Series.
(pp. pp. 2380-2383).
Proceedings of AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture: New York, NY, USA.
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Abstract
Cerebral diseases occur frequently, and the complex pathophysiology involves abnormal changes in the parenchyma, blood vessels and cerebrospinal fluid circulation. MRI-coupled numerical simulations can comprehensively capture differences in fluid transport, and further quantitatively describe the functional changes in the brain. Multiple-network PoroElastic Theory (MPET) introduces a new method based on MR sequences to explore changes in the brain with multiple scales of fluids considered. In this research, diffusion tensor imaging (DTI) was used to optimize the segmentation of gray matter and white matter, and then to construct finite element meshes. Cerebral blood perfusion, as a biomarker for cerebral diseases and a core output under MPET simulations, shows consistency between clinical perfusion images and MPET simulations with more detailed regional information.
| Type: | Proceedings paper |
|---|---|
| Title: | Cerebral Perfusion of Multiple-Network Poroelastic Model by Integrating Fractional Anisotropy |
| Event: | AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture |
| ISBN-13: | 9781450385046 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3495018.3501107 |
| Publisher version: | https://doi.org/10.1145/3495018.3501107 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Cerebral blood flow, blood perfusion, poroelasticity, multiple fluid networks, magnetic resonance imaging, fractional anisotropy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10217729 |
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