Presentation description

Vessel wall imaging of carotid arteries is crucial in clinical evaluations due to their significant role in supplying oxygenated blood to the brain and their susceptibility to atherosclerosis and other clinical events [1]. However, the current clinical practices often rely on manual qualitative assessments, which are time-consuming and subject to inter-rater variability. Hence, there is a pressing need to develop automated image processing tools that enable quantitative analysis of vessel wall imaging data. Techniques such as computational fluid dynamics (CFD) analysis, combined with precise thickness measurements, offer valuable insights into stenosis, plaque formation, and disease progression in carotid arteries [2].||This work aims to develop a Python-based pipeline leveraging the Vascular Modeling Toolkit (VMTK) library to generate centerlines and meshes from three-dimensional segmented carotid artery data [3], automatically and efficiently. Automation of this process streamlines workflow and reduces the chance of human error. By automating the centerline and mesh generation, we lay the foundation for extracting and reporting morphological features such as thickness measures and CFD simulations, which are critical for understanding carotid artery behavior and pathology [2]. The developed Python scripts demonstrate the capability for rapid and accurate centerline and mesh generation, streamlining the steps needed to set up CFD simulations as well as facilitation of vessel wall imaging quantitative analysis. In the future, we aim to use these tools to evaluate longitudinal vessel wall imaging data on patients with Intraplaque hemorrhage (IPH) to achieve a better understanding of vascular disease progression in the carotid. ||