Robust Matching of 3D Lung Vessel Trees

In order to study ventilation or to extract other functional information of the lungs, intra-patient matching of scans at a different inspiration level is valuable as an examination tool. In this paper, a method for robust 3D tree matching is proposed as an independent registration method or as a guide for other, e.g. voxel-based, types of registration. For the first time, the 3D tree is represented by intrinsic matrices, reference frame independent descriptions containing the geodesic or Euclidean distance between each pair of detected bifurcations. Marginalization of point pair probabilities based on the intrinsic matrices provides soft assign correspondences between the two trees. This global correspondence model is combined with local bifurcation similarity models, based on the shape of the adjoining vessels and the local gray value distribution. As a proof of concept of this general matching approach, the method is applied for matching lung vessel trees acquired from CT images in the inhale and exhale phase of the respiratory cycle.