Converting Medical Imaging to Pre-Processing Computational Grids

Eisa Mohammed Y Alyaqoub, “Converting Medical Imaging to Pre-Processing Computational Grids”
Mentor: Mahsa Dabagh, Biomedical Engineering

One of the essential medical technologies that are used these days to study different diseases is medical imaging. To take a critical advantage from medical imaging in studying a specific disease, image segmentation is the first step that must be done as preprocessing. Image segmentation is considered the most essential medical imaging process as it extracts the region of interest (ROI) through a semiautomatic or automatic process. It divides an image into areas based on a specified description, such as segmenting body organs/tissues in the medical applications for border detection, tumor detection/segmentation, and mass detection. Conversion of medical images to pre-processed computational grids will dramatically increase the size of future retrospective studies while widening the scope of computational fluid models to both larger data sets and organs with complex anatomy including small capillaries. In this study, patient-specific images of growing cerebral aneurysma at different growth stages are segmented and reconstructed. Computational meshes representing 3D topology are created from CT-scan data sets corresponding to the pre- and post-stenting stages of 4 patients using the image processing software Materialise Mimics (Materialise, Leuven, Belgium). Blood flow in these computational models will be then simulated using the Palabos an open source Computational Fluid Dynamics (CFD) library. We will examine the impact of stent deployment on blood flow distribution, flow structure, wall shear stress (WSS), and oscillatory shear index (OSI) in pre- and post-stent deployment stages. Our findings will lead to better understand how hemodynamics is linked the growth of aneurysm and how stenting may help slow down/prevent the growth of aneurysm.

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