PNNL

Abstract

The geometry of arterial and vascular trees is a strong determinant of blood flow in health and disease. For example, there is a strong geometric component to atherosclerosis: lesions are preferentially located at bifurcation points and regions of high curvature. The influence of these local structures on recirculation and deleterious shear stresses and their role in plaque development is now widely accepted. Over time, researchers have turned to MR, CT or biplane images of vascular trees in an effort to faithfully capture these features in their simulations. Historically, this has taken the form of labor intensive manual reconstructions from morphometric measurements based on the centerline, whereby small idealized subsets of vascular trees are developed into computational grids. More recently, with improved imaging and improved image processing and geometric reconstruction algorithms, researchers have begun to develop geometrically accurate computational models directly from the medical images, enabling the resolution of small scale geometric features visible in the image dataset. This chapter provides an overview of contemporary methods for image processing, centerline detection, boundary condition definition and grid generation of both clinical and research images of cardiovascular structures.