Prediction of Respiratory Motion Using A Statistical 4D Mean Motion Model

In this paper we propose an approach to generate a 4D sta-tistical model of respiratory lung motion based on thoracic 4D CT data of different patients. A symmetric diffeomorphic intensity–based registra-tion technique is used to estimate subject–specific motion models and to establish inter–subject correspondence. The statistics on the diffeomor-phic 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 individ-ual organ motion. The prediction is evaluated with respect to landmark and tumor motion. Mean absolute differences between model–based pre-dicted landmark motion and corresponding breathing–induced landmark displacements as observed in the CT data sets are 3.3 ± 1.8 mm consid-ering 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 provid-ing 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.