Super resolution ultrasound image filtering with machine learning to reduce the localization error
Conference paper
Harput, S., Fong, L.H., Stanziola, A., Zhang, G., Toulemonde, M., Zhou, J., Christensen-Jeffries, K., Brown, J., Eckersley, R., Grisan, E., Dunsby, C. and Tang, M. (2019). Super resolution ultrasound image filtering with machine learning to reduce the localization error. IEEE International Ultrasonics Symposium 2019. Glasgow 09 2009 - 06 Oct 2019 Institute of Electrical and Electronics Engineers (IEEE).
Authors | Harput, S., Fong, L.H., Stanziola, A., Zhang, G., Toulemonde, M., Zhou, J., Christensen-Jeffries, K., Brown, J., Eckersley, R., Grisan, E., Dunsby, C. and Tang, M. |
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Type | Conference paper |
Abstract | 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. |
Year | 2019 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Accepted author manuscript | License File Access Level Open |
Publication dates | |
06 Oct 2019 | |
Publication process dates | |
Accepted | 14 Jun 2019 |
Deposited | 29 Oct 2019 |
https://openresearch.lsbu.ac.uk/item/88559
Download files
Accepted author manuscript
Super-Resolution Filtering_v4.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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