Cardiac Motion Analysis from Ultrasound Sequences Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Estimating myocardial motion by 4D image warping
Pattern Recognition
Detection of left ventricular motion abnormality via information measures and Bayesian filtering
IEEE Transactions on Information Technology in Biomedicine
Regional heart motion abnormality detection via information measures and unscented kalman filtering
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive least-squares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contour-based description of the object's boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frame-to-frame relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system's output are presented for quantitative analysis. Validation of the system is performed by comparing computed trajectory estimates with the trajectories of physical markers implanted in the LV wall. Sample case studies of marker trajectory comparisons are presented. Ensemble statistics from comparisons with 15 marker trajectories are acquired and analyzed. A multiframe temporal model without spatial periodicity constraints was determined to provide excellent performance with the least computational cost. A multiframe spatiotemporal model provided the best performance based on statistical standard deviation, although at significant computational expense