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).
AuthorsHarput, 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.
TypeConference 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.

Year2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
Publication dates
Print06 Oct 2019
Publication process dates
Accepted14 Jun 2019
Deposited29 Oct 2019
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https://openresearch.lsbu.ac.uk/item/88559

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