Registration of 4D Time-Series of Cardiac Images with Multichannel Diffeomorphic Demons
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
4D MAP Image Reconstruction Incorporating Organ Motion
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Hierarchical adaptive local affine registration for respiratory motion estimation from 3-D MRI
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Consistent estimation of cardiac motions by 4D image registration
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Temporal groupwise registration for motion modeling
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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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.