Visualization of Anatomic Tree Structures with Convolution Surfaces

Abstract

We present a method for visualizing anatomic tree structures, such as vasculature and bronchial trees based on clinical CT- or MR data. The vessel skeleton as well as the diameter information per voxel serve as input. Our method adheres to these data, while producing smooth transitions at branchings and closed, rounded ends by means of convolution surfaces. We discuss the filter design with respect to irritating bulges, unwanted blending and the correct visualization of the vessel diameter. Similar to related work our method is based on the assumption of a circular cross-section of vasculature. In contrast to other authors we employ implicit surfaces to achieve high quality visualization. The method has been applied to a large variety of anatomic trees and produces good results. The time to construct a geometric model is reduced by means of different bounding volumes and careful choice of parameters for polygonization.