Physics-Based Modeling of Aortic Wall Motion from ECG-Gated 4D Computed Tomography

Recent advances in electrocardiogram (ECG)-gated Computed Tomography (CT) technology provide 4D (3D+T) information of aortic wall motion in high spatial and temporal resolution. However, imaging artifacts, e.g. noise, partial volume effect, misregistration and/or motion blurring may preclude its usability in many applications where accuracy and reliability are concerns. Although it is possible to find correspondence through tagged MRI or echo or image registration, it may be either inconsistent to the physics or difficult to utilize data from all frames. In this paper, we propose a physics-based filtering approach to construct a dynamic model from these 4D images. It includes a state filter that corrects simulated displacements from an elastic finite element model to match observed motion from images. In the meantime, the model parameters are refined to improve the model quality by applying a parameter filter based on ensemble Kalman filtering. We evaluated the performance of our method on synthetic data where ground-truths are available. Finally, we successfully applied the method to a real data set.