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Simulated ultrasound (US) data are widely used in echocardiography to develop and validate rapidly growing convolutional neural networks (CNNs) based learning algorithms for image processing and analysis. In this context, a large and diverse database of synthetic US scans is considered vital for CNN training purposes, as clinical US data are scarce and difficult to access. Major hurdles in creating an extensive database are the long US simulation time and unstable heart models for extreme parameter settings. Here, we developed and implemented a cardiac US simulation pipeline that kinematically connects two state-of-the-art solutions in the field of US simulation (COLE) and cardiac modelling (CircAdapt), benefiting from the fast simulation time of the convolution-based ultrasound simulator and stability of the mechanical heart model to produce 2D synthetic cardiac US recordings. Furthermore, using our pipeline, we generated diverse set of 600 2D synthetic cardiac US recordings of healthy and heart failure virtual patients with variations in the shapes, motion patterns, and functions of the heart, along with their ground truth 2D myocardial velocity profiles and deformation curves. The resulting database is a potential tool for augmenting training databases of machine learning based US image processing algorithms. [Work funded by European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 860745.] |
