Second International Workshop on Pulmonary Image Analysis

In this paper we propose an approach to generate a 4D statistical model of respiratory lung motion based on thoracic 4D CT data of different patients. A symmetric diffeomorphic intensity–based registration technique is used to estimate subject–specific motion models and to establish inter–subject correspondence. The statistics on the diffeomorphic transformations are computed using the Log–Euclidean framework. We present methods to adapt the genererated statistical 4D motion model to an unseen patient–specific lung geometry and to predict individual organ motion. The prediction is evaluated with respect to landmark and tumor motion. Mean absolute differences between model–based predicted landmark motion and corresponding breathing–induced landmark displacements as observed in the CT data sets are 3.3± 1.8 mm considering motion between end expiration to end inspiration, if lung dynamics are not impaired by lung disorders. The statistical respiratory motion model presented is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in the fields of radiation therapy and image guided diagnosis.