Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration

Abstract

Tracking lung tissues during the respiratory cycle has been a challenging task for diagnostic CT and CT-guided radiotherapy. We propose an intensity- and landmark-based image registration algorithm to perform image registration and warping of 3D pulmonary CT image data sets, based on consistency constraints and matching corresponding airway branchpoints. In this paper, we demonstrate the effectivenss and accuracy of this algorithm in tracking lung tissues by both animal and human data sets. In the animal study, the result showed a tracking accuracy of between 50% functional residual capacity (FRC) and 85% total lung capacity (TLC) for 12 metal seeds implanted in the lungs of a breathing sheep under precise volume control using a pulmonary ventilator. Visual inspection of the human subject results revealed the algorithm's potential not only in matching the global shapes, but also in registering the internal structures (e.g., oblique lobe fissures, pulmonary artery branches, etc.). These results suggest that our algorithm has significant potential for warping and tracking lung tissue deformation with applications in diagnostic CT, CT-guided radiotherapy treatment planning, and therapeutic effect evaluation.