Abstract
Super-resolution (SR) is a fascinating frontier in medical ultrasound (US) imaging offering the possibility of studying biological activity at spatiotemporal scales beyond the classical diffraction limit [1]. The key to SR is reliable detection and subsequent tracking of centroids of US contrast agents, over thousands of frames [1]. However, methods to overcome motion artefacts and background tissue speckle impose computational overhead [2]; in addition to physical tradeoffs in data acquisition [1] [3]; thereby limiting biological applications to larger vessels with high blood flow rates [1]. The real-time or online nature of ultrasound imaging is sacrificed due to the offline nature of super-resolution processing methods [1]. In this work, we explore combinations of current machine vision algorithms, popular for similar object detection and tracking problems in optical imaging [4] - towards near real-time [5] super-resolution ultrasound imaging. We report encouraging results motivating further work towards improving state-of-the-art machine vision models designed for online, real-time, detection and tracking for ultrasound super-resolution.
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