Probabilistic framework for tracking in artifact-prone 3D echocardiograms

Abstract 

The analysis of echocardiograms, whether visual or automated, is often hampered by ultrasound artifacts which obscure the moving myocardial wall. In this study, a probabilistic framework for tracking the endocardial surface in 3D ultrasound images is proposed, which distinguishes between visible and artifact-obscured myocardium. Motion estimation of visible myocardium relies more on a local, data-driven tracker, whereas tracking of obscured myocardium is assisted by a global, statistical model of cardiac motion. To make this distinction, the expectation-maximization algorithm is applied in a stationary and dynamic frame-of-reference. Evaluation on 35 three-dimensional echocardiographic sequences shows that this artifact-aware tracker gives better results than when no distinction is made. In conclusion, the proposed tracker is able to reduce the influence of artifacts, potentially improving quantitative analysis of clinical quality echocardiograms.

Keywords: Echocardiography, Artifact detection, Segmentation, Expectation maximization