Spatio-Temporal Alignment of Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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
Event Dynamics Based Temporal Registration
IEEE Transactions on Multimedia
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MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
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WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
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FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
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MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Improving registration using multi-channel diffeomorphic demons combined with certainty maps
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Computer Vision and Image Understanding
IPCAI'13 Proceedings of the 4th international conference on Information Processing in Computer-Assisted Interventions
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In this paper, we propose a generic framework for inter-subject non-linear registration of 4D time-series images. In this framework, spatio-temporal registration is defined by mapping trajectories of physical points as opposed to spatial registration that solely aims at mapping homologous points. First, we determine the trajectories we want to register in each sequence using a motion tracking algorithm based on the Diffeomorphic Demons algorithm. Then, we perform simultaneously pairwise registrations of corresponding time-points with the constraint to map the same physical points over time. We show this trajectory registration can be formulated as a multichannel registration of 3D images. We solve it using the Diffeomorphic Demons algorithm extended to vector-valued 3D images. This framework is applied to the inter-subject non-linear registration of 4D cardiac CT sequences.