Super resolution ultrasound image filtering with machine learning to reduce the localization error

Localization-based super-resolution imaging re-quires accurate detection of spatially isolated microbubbles. The reason for this requirement is that interfering or overlapping signals resulting from multiple microbubbles within the resolu-tion limit can cause position errors. In addition to this, noise and artefacts (e.g. residual tissue signal after tissue-microbubble separation) further reduce the quality and hence the spatial resolution in SR imaging. Therefore, correctly identifying the echoes as noise, single microbubble, multiple microbubbles, or artefact is important. In this study, we are demonstrating the use of fast classification methods for identification and rejection of non-single microbubble echoes.