Large-Scale Simulation of Realistic Cardiac Ultrasound Data With Clinical Appearance: Methodology and Open-Access Database

Cardiac ultrasound imaging is widely used in the clinical setting. Deep learning algorithms have shown increased potential in automating routine clinical tasks for improved diagnosis and prognosis. However, the lack of large and diverse training databases hinders their employment on a full scale in clinics. Difficulties associated with creating large training databases include scarcity of clinical data, privacy related data access concerns and labor-intensive ground-truth annotations. To overcome these issues, realistic cardiac ultrasound simulation is considered advantageous. Nevertheless, our state-of-the-art cardiac ultrasound simulation pipeline is limited to small-scale databases particularly because of the underlying computationally complex finite-element heart model. In this paper, we developed a realistic simulation pipeline to facilitate large-scale synthetic cardiac ultrasound datasets. This pipeline utilizes a well-validated and computationally efficient lump-parameter heart model for generating cardiovascular mechanics, and the convolution based ultrasound simulator COLE for short simulation times. We implemented the proposed pipeline to create a large open-access database of 2-D realistic cardiac ultrasound recordings with diversity in the geometries, ultrasound speckle texture and motions of the left ventricle. Quantitative assessment of the simulated data demonstrated that the synthetic cardiac ultrasound frames contained realistic speckle patterns. Moreover, the left ventricular motion in the simulated frames was tracked using the medical imaging tracking toolbox and was shown to closely match the kinematically imposed ground-truth left ventricular motion. The presented pipeline is valuable for creating large and diverse ground-truth training databases. The resulting database is a potential augmentation tool for machine learning-based ultrasound data processing algorithms and made publicly available.