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

Jan Ehrhardt, René Werner, Alexander Schmidt-Richberg, Heinz Handels

Abstract

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.
Original languageEnglish
Pages3-14
Number of pages12
Publication statusPublished - 2009
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention - London, United Kingdom
Duration: 20.09.200924.09.2009
Conference number: 77822

Conference

Conference12th International Conference on Medical Image Computing and Computer-Assisted Intervention
Abbreviated title MICCAI 2009
Country/TerritoryUnited Kingdom
CityLondon
Period20.09.0924.09.09

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