PNNL

Abstract

Fast and accurate reconstruction of imaging-derived geometries and subsequent quality mesh generation for biomedical computation are enabling technologies for both clinical and research simulations. Often, the transformation of image to mesh is the rate-limiting step, requiring arduous manual manipulations. A challenging part of this process is the introduction of computable, orthogonal boundary patches, namely the outlets, into treed structures, such as vasculature, arterial or airway trees. Herein, we present efficient and robust algorithms for automatically identifying and truncating the outlets for complex biomedical geometries. Our approach is based on a conceptual decomposition of objects into three different types of local structures, including tips, segments, and branches, where the tips determine the outlets. We define the tips by introducing a novel geometric concept called the average interior center of curvature (AICC), and identify those that are physically stable and numerically noise resistant through successive inflation and deflation tests. We compute well-defined orthogonal planes, which truncate the tips into outlets. The rims of the outlets are then connected into curves, and the outlets are then closed using Delaunay triangulation with the curves as the boundary constraints. Our approach is efficient, with near linear time complexity. We illustrate the effectiveness and robustness of our approach with a variety of complex lung and coronary artery geometries.