Multiframe nonrigid motion analysis with anisotropic spatial constraints: applications to cardiac image analysis

Proper spatial and temporal constraints are essential for image-based motion recovery of deforming objects. Since biological organs, such as the heart, are typically composed of fibrous tissues of anisotropic nature, one must adopt realistic spatial models, in addition to those important considerations for temporal modeling, in order to properly regularize the object behavior for kinematics recovery. We present a biomechanically constrained state space analysis framework for the multiframe estimation of the heart motion and deformation. While the anisotropic physical constraints enforce spatial regulations on the myocardial behavior and spatial filtering of the image data measurements, statistical filtering techniques impose temporal constraints to incorporate multiframe information. Implemented within a mesh-free particle representation and computation framework, excellent experimental results are achieved for both synthetic data with known ground truth and canine magnetic resonance image sequences with known clinical gold standard.