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. We describe a predictive technique which can predict the amplitude of a breathing cycle before it has finished. The predicted amplitude is used to interpolate between the motion estimates of the submodels to tune the adaptive model to the current breathing pattern. The proposed technique is validated on affine motion models formed from cardiac magnetic resonance imaging (MRI) datasets acquired from seven volunteers and one patient. The amplitude prediction technique showed errors of 1.9-6.5 mm. The combined predictive and adaptive technique showed 3-D motion prediction errors of 1.0-2.8 mm, which represents an improvement in modelling performance of up to 40% over a standard nonadaptive single parameter motion model. We also applied the combined technique in a clinical setting to test the feasibility of using it for respiratory motion correction of roadmaps in image-guided cardiac catheterisations. In this clinical case we show that 2-D registration errors due to respiratory motion are reduced from 7.7 to 2.8 mm using the proposed technique.