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.
Original language | English |
---|---|
Title of host publication | Proceedings of the Workshop on Biomedical Image Registration (WBIR) |
Publisher | Springer |
Pages | 222 - 233 |
Number of pages | 12 |
Volume | 6204 LNCS |
ISBN (Print) | 978-3-642-14365-6 |
Publication status | Published - 2010 |
Event | Workshop on Biomedical Image Registration (WBIR) - Lubeck, Germany Duration: 1 Jan 2010 → … |
Conference
Conference | Workshop on Biomedical Image Registration (WBIR) |
---|---|
Country/Territory | Germany |
City | Lubeck |
Period | 1/01/2010 → … |