Prediction framework for statistical respiratory motion modeling

Authors:
Tobias Klinder;Cristian Lorenz;Jörn Ostermann
Affiliations:
Philips Research North America, Briarcliff Manor, NY;Philips Research Europe, Hamburg, Germany;Institut für Informationsverarbeitung, Leibniz University of Hannover, Germany
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.