Key result
A new particle filtering approach for tracking MRI tag intersections demonstrated improved robustness and accuracy in preliminary animal imaging studies.
A novel particle filtering method for tracking MRI tag intersections shows promise for more robust and accurate quantitative analysis of regional heart dynamics in preliminary animal studies.
Hypothesis-generating for cardiac MRI motion tracking; human validation required before clinical consideration.
Myocardial tagging using magnetic resonance imaging (MRI) is a well-known noninvasive method for studying regional heart dynamics. While it offers great potential for quantitative analysis of a variety of kinematic and kinetic parameters, its clinical use has so far been limited, mainly due to mediocre performance of existing tag tracking algorithms under poor imaging conditions. In this paper we propose a new approach to tracking of MRI tag intersections. It is based on a Bayesian estimation framework, implemented by means of particle filtering, and combines information about heart dynamics, the imaging process, and tag appearance. Since at any time point it optimally incorporates all available information, it can be expected to be more robust and accurate. This is demonstrated by results of preliminary experiments on image sequences from (small) animal imaging studies.
No takes yet. Share an insight, caveat, or question.
Smal et al. (2010) studied Myocardial tagging using MRI. Particle filtering method for tracking MRI tag intersections vs. Existing tag tracking algorithms was evaluated on Tracking robustness and accuracy. A new particle filtering approach for tracking MRI tag intersections demonstrated improved robustness and accuracy in preliminary animal imaging studies.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: