Key result
A recursive comb filtering approach for estimating cardiac motion was developed and compared with MR contrast marker trajectories, confirming 2-D analysis is useful for algorithm development.
A recursive comb filtering framework using harmonic estimation and flow vector computation can model and estimate cardiac motion from image sequences.
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Supports 2D cardiac motion algorithm development; leaves open prospective clinical validation before practice use.
A framework for temporal analysis of left ventricular (LV) endocardial wall motion is presented. This approach uses harmonic estimation to model the periodic nature of cardiac motion. A method for flow vector computation is presented which defines a relationship between image-derived, shape-based correspondences and a more desirable, smoothly varying, set of correspondences. A recursive filter is then constructed which takes into consideration this relationship as well as knowledge of temporal trends. The trajectories of magnetic resonance (MR) contrast markers implanted in the LV wall are compared with trajectories estimated by the filter. The results of this comparison confirm that although cardiac motion is a 3-D problem, 2-D analysis provides a rich testing ground for algorithm development.
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McEachen et al. (2002) studied Cardiac motion analysis. Recursive comb filtering vs. Magnetic resonance (MR) contrast markers was evaluated on Comparison of trajectories estimated by the filter with trajectories of MR contrast markers. A recursive comb filtering approach for estimating cardiac motion was developed and compared with MR contrast marker trajectories, confirming 2-D analysis is useful for algorithm development.
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