Abstract
Motion models have been widely applied as a solution to the problem of organ motion in both image acquisition and image guided interventions. The traditional approach to constructing motion models from dynamic images involves first coregistering the images to produce estimates of motion parameters, and then modelling the variation of these parameters as functions of a surrogate value or values. Errors in this approach can result from inaccuracies in the image registrations and in the modelling process. In this paper we describe an approach in which the registrations of all images and the modelling process are performed simultaneously. Using numerical phantom data and 21 dynamic magnetic resonance imaging (MRI) datasets acquired from 7 volunteers and 7 patients, we demonstrate that our new technique results in an average reduction in motion model errors of 11.5% for the phantom experiments and 1.8% for the MRI experiments. This approach has the potential to improve the accuracy of motion estimates for a range of applications.
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King, A.P., Buerger, C., Schaeffter, T. (2010). Cardiac Respiratory Motion Modelling by Simultaneous Registration and Modelling from Dynamic MRI Images. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds) Biomedical Image Registration. WBIR 2010. Lecture Notes in Computer Science, vol 6204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14366-3_20
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DOI: https://doi.org/10.1007/978-3-642-14366-3_20
Publisher Name: Springer, Berlin, Heidelberg
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