3D cardiac motion tracking using Robust Point Matching and meshless deformable models

We propose a novel 3D motion estimation approach integrating the robust point matching (RPM) and meshless deformable models. In our study, we first use the Gabor filters to generate phase maps of short axis (SA) and long axis (LA) tagged MRI sequences. Then we use the RPM to track the heart motion sparsely at intersections of tag grids in these image sequences, using both intensity gradient and phase information. Next, the new meshless deformable model is used to recover the dense 3D motion of the myocardium temporally during the cardiac cycle. The deformable model is driven by external forces computed at tag intersections based on the RPM motion tracking and keeps a consistent but flexible topology during the deformation using internal constraint forces calculated by the moving least squares (MLS) method. The deformable model recovers the global deformation of the LV such as rotation, contraction and twisting by integrating global deformation parameters over the volume. The new model avoids the singularity problem of mesh-based deformable models and is capable of tracking deformation efficiently with the sparse external forces derived from tagging line intersections. We test the performance of the new approach on in vivo heart data of healthy subjects and patients. The experimental results show that our new method can fully recover the myocardium motion and strain in 3D.