Endocardial Left Ventricle Feature Tracking and Reconstruction from Tri-plane Trans-esophageal Echocardiography Data

摘要

Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 60° to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined workflow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed workflow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined workflow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and "on-the-fly" computer-assisted assessment of ejection fraction for cardiac function monitoring.