An adaptive and predictive respiratory motion model for image-guided interventions: theory and first clinical application.

This paper describes a predictive and adaptive single parameter motion model for updating roadmaps to correct for respiratory motion in image-guided interventions. The model can adapt its motion estimates to respond to changes in breathing pattern, such as deep or fast breathing, which normally would result in a decrease in the accuracy of the motion estimates. The adaptation is made possible by interpolating between the motion estimates of multiple submodels, each of which describes the motion of the target organ during cycles of different amplitudes.