King's College London

Research portal

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Research output: Contribution to journalArticle

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
JournalIEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
Volume64
Issue number11
Early online date17 Aug 2017
DOIs
Publication statusPublished - Nov 2017

Documents

  • Microbubble Axial Localization Errors_CHRISTENSEN-JEFFRIES_Publishedonline17August2017_GREEN AAM

    clean_file_for_Xplore_LocError_PDFFiledoc9_1_.pdf, 1.85 MB, application/pdf

    14/11/2017

    Accepted author manuscript

    Unspecified

    (c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

King's Authors

Abstract

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.

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454