Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer such that the 2D imaging plane properly aligns with the heart for standard measurement views, and is thus dependent on the operator’s skills. In this work, we propose a deep learning-based tool that provides real-time feedback on how to move the transducer to obtain the required views. We believe this method can aid less-experienced users to acquire recordings of better quality for measurements and diagnosis, and to improve standardization of images for more experienced users. Training data was generated by slicing 3D ultrasound volumes, which permits to simulate movements of a transducer and 2D imaging plane. Each slice was labelled with an anatomical reference obtained through a semi-automatic annotation procedure, which allowed us to generate substantial amounts of training data. The method was validated and tested on 2D images from several datasets representative of a prospective clinical setting. We proposed a new metric to score the correctness of the transducer movement feedback according to several given criteria, and achieved a success rate of 75% for all models and 95% for the rotational movement. A real-time prototype application was developed based on data streaming from a clinical ultrasound system, which demonstrated the ability of the method to robustly predict the apical rotation and tilt of the 2D ultrasound image plane relative to the heart.
