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Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Research output: Contribution to journalArticlepeer-review

Kirsten Christensen-Jeffries, Sevan Harput, Jemma Brown, Peter N.T. Wells, Paul Aljabar, Chris Dunsby, Meng Xing Tang, Robert J. Eckersley

Original languageEnglish
Pages (from-to)1644-1654
Issue number11
Early online date17 Aug 2017
Accepted/In press6 Jul 2017
E-pub ahead of print17 Aug 2017
PublishedNov 2017


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King's Authors


Acoustic super-resolution imaging has allowed visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7 cm depth and 2.3 MHz center frequency. We corroborate these findings with simulated results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental cross-sectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection and 2D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as 900 μ m at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is therefore expected to considerably improve the accuracy of super-resolution.

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