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.

Additional Metadata
Keywords Particle filtering, Tagged MRI, Tracking
Persistent URL dx.doi.org/10.1109/ISBI.2010.5490302, hdl.handle.net/1765/85669
Conference 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Citation
Smal, I, Niessen, W.J, & Meijering, E. (2010). Particle filtering methods for motion analysis in tagged MRI. Presented at the 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010. doi:10.1109/ISBI.2010.5490302