Three-dimensional velocity mapping of lung motion using vessel bifurcation pattern matching.

We present a new quantification technique for three-dimensional (3D) lung motion by means of tracking the anatomical features inside the lung using a set of sequential 3D-CT images (a 4D-CT image). The method is based on the conservation of topology, such as connections and junctions of vessels, during the motion. Lung CT images are used to do lung volume modeling, lung vessel extracting and thinning, and coordinates of vessel bifurcations are derived as feature points. Such feature points are tracked in a series of 3D-CT images, i.e., the points are individually tracked between two successive 3D-CT images, in which the lung is deformed. Consequently, 3D displacement vectors are obtained. The feature point tracking is carried out using point pattern matching with a probabilistic relaxation method. We examined this technique using a lung 3D-CT image and artificially deformed one, and separately scanned CT images for a rigid bifurcation phantom. The studies estimated that the error of the vectors is within approximately 1 voxel, i.e., 1 mm or less. Therefore, the accuracy is expected to be high enough for radiation therapy. This technique enables us to quantify realistic 3D organ motion without any fiducial markers. It can be applied to the quantification of tumor (target volume) deformation by gridding interpolation into all voxels. We expect it to be useful for dose estimation in mobile organs and for 4D treatment planning in radiation therapy.