Pulmonary motion tracking from 4D-CT images using a 3D-KLT tracker

We propose a new method for lung-motion tracking and its quantification from 4-dimensional X-ray computed tomographic (4D-CT) images. This method uses an enhanced 3D-KLT tracker. An advantage of our method is that it can find many feature points (regions) for tracking that are not restricted to the bifurcation points of bronchi or vessels. The feature point extraction algorithm depends only on image gradients. Moreover, our method adopts a hierarchical tracking based on pyramidal image structure. This provides robustness for large movements of the objects. Lung motion is quantified by tracking a large number of feature points in the lung. In this paper, we first evaluate the performance of our proposed method for artificial 4D-CT images and then describe quantification results of real 4D-CT images. Our experimental results clearly show that lung movement is not modeled by a simple translation but by an oval pattern.