Model Based Tracking of Anatomic Structures

This work serves as a proof-of-concept study for modeling and tracking the cardiac surface deformations by means of a low-order probabilistic model from streaming magnetic resonance (MR) images. Cardiac surface motion is represented as a stochastic dynamic system. Deformable models are utilized to introduce shape prior to control the extent of the deformations. Dynamic model of the system uses adaptive filters to model and predict complex heart motion. Particle filters are employed to recursively estimate current system state over time. The proposed method is applied to recover biventricular deformations and validated with a numerical phantom and multiple real cardiac MRI datasets. The algorithm is evaluated with multiple experiments using fixed and varying image slice planes at each time step.

The main contributions of this work are twofold. First, it presents a framework for the tracking of whole cardiac surface from a time sequence of single image slices. Second, it employs adaptive filters to incorporate motion information in the tracking of nonrigid cardiac surface motion for temporal coherence.

Article

• Deformable Cardiac Surface Tracking by Adaptive Estimation Algorithms
  E. E. Tuna, D. Franson, N. Seiberlich, and M.C. Cavusoglu
  Scientific Reports, 13, 1387, 2023. [pdf]