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Large-Scale Hemodynamic Analysis of Cerebral Arterial Tree with Parametric Mesh Generation Technique

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posted on 28.11.2018, 00:00 authored by Mahsa Ghaffari
In the last decade, detailed hemodynamic analysis of blood flow in pathological segments close to aneurysm and stenosis has provided physicians with invaluable information about the local flow patterns leading to vascular disease. However, these diseases have both local and global effects on the circulation of the blood within the cerebral tree. The bottle-neck to develop a global model is that computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography is an error-prone, operator-dependent, and very time-consuming task. The aim of this project is to generate automatic meshing procedure to extend subject-specific hemodynamic simulations to the large-scale cerebral arterial tree with hundreds of bifurcations and vessels, as well as evaluate hemodynamic risk factors and waveform shape characteristics throughout the cerebral arterial trees. For this purpose, we introduce a parametric mesh generation method which can automatically create structured meshes for patient-specific models of cerebral arterial trees. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using modified Hausdorff distance. A global map of cerebral arterial blood flow distribution revealed regions of low to high hemodynamic risk contributing to the development of intracranial aneurysms or atherosclerosis. In conclusion, this project can be used to quantify hemodynamic risk and morphological analysis for prediction of cerebral vascular tree diseases from large cerebral arteries down to small pial vessels.

History

Advisor

Linninger, Andreas A

Chair

Linninger, Andreas A

Department

Bioengineering

Degree Grantor

University of Illinois at Chicago

Degree Level

Doctoral

Committee Member

Charbel, Fady T Zhou, Xiaohong Joe Khetani, Salman Singh, Meenesh R

Submitted date

August 2018

Issue date

18/07/2018