A Biomechanical Model of Muscle Contraction
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
3D Cardiac Deformation from Ultrasound Images
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
FIMH'07 Proceedings of the 4th international conference on Functional imaging and modeling of the heart
Physiome model based state-space framework for cardiac kinematics recovery
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A learning framework for the automatic and accurate segmentation of cardiac tagged MRI images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Incorporating low-level constraints for the retrieval of personalised heart models from dynamic MRI
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Driving dynamic cardiac model adaptation with MR-tagging displacement information
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Cardiac microstructure estimation from multi-photon confocal microscopy images
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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We present a method for cardiac motion recovery using the adjustment of an electromechanical model of the heart to cine MRI. This approach is based on a proactive model which consists in a constrained minimisation of an energy coupling the model and the data. The presented method relies on specific image features in order to constrain the motion of the endocardia and epicardium and impose boundary conditions at the base. Thus, image intensity and gradient information are used to constrain the motion of the myocardium surfaces while a 3D block matching technique leads to the motion estimation of base vertices. Finally, we show that the implicit time integration of those forces and personalised boundary conditions lead to a better cardiac motion recovery from cine-MR images.