Objective: This paper aims to develop a method for achieving micrometre axial scatterer localization for medical ultrasound, surpassing the inherent, pulse length dependence limiting ultrasound imaging. Methods: The method, directly translated from cellular microscopy, is based on multi-focal imaging and the simple, aberration dependent, image sharpness metric of a single point scatterer. The localization of a point scatterer relies on the generation of multiple overlapping sharpness curves, created by deploying three foci during receive processing, and by assessing the sharpness values after each acquisition as a function of depth. Each derived curve peaks around the receive focus and the unique position of the scatterer is identified by combining the data from all curves using a maximum likelihood algorithm with a calibration standard. Results: Simulated and experimental ultrasound point scatter data show that the sharpness method can provide scatterer axial localization with an average accuracy down to 10.21 μm (≈λ/21) and with up to 11.4 times increased precision compared to conventional localization. The improvements depend on the rate of change of sharpness using each focus, and the signal to noise ratio in each image. Conclusion: Super-resolution axial imaging from optical microscopy has been successfully translated into ultrasound imaging by using raw ultrasound data and standard beamforming. Significance: The normalized sharpness method has the potential to be used in scatterer localization applications and contribute in current super-resolution ultrasound imaging techniques.
- School of Engineering & Physical Sciences - Associate Professor
- School of Engineering & Physical Sciences, Institute of Biological Chemistry, Biophysics and Bioengineering - Associate Professor
Person: Academic (Research & Teaching)
Diamantis, K., Greenaway, A. H., Anderson, T., Jensen, J. A., Dalgarno, P. A., & Sboros, V. (2017). Super-resolution Axial Localization of Ultrasound Point Sources Using Multi-focal Imaging. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2017.2769164