MR tag surface tracking using a spatio-temporal filter/interpolator

Magnetic resonance imaging provides the unique capability to produce artificial, high-contrast features called “tags” that are invaluable for tracking tissue motion. Each tag corresponds to an initially planar surface embedded in the tissue that deforms with tissue motion. By tracking tag surface deformation, quantitative analysis of tissue motion can be performed. Here, the authors present a method for tag surface tracking that applies specifically to tag surfaces embedded in the wall of the left ventricle. The method addresses two key issues: first, the full spatial extent of tag surfaces in 3-D space must be inferred from 2-D images, and second, within the images, noise leads to uncertainty in tag positions. The authors address these issues by framing tag surface tracking as an estimation problem given the observed image data. The estimates are obtained using a stochastic model of tag deformation and a recursive algorithm that simultaneously filters over time and smoothly interpolates between images