Edinburgh Research Explorer

Development of Super-resolution Sharpness-based Axial Localization for Ultrasound Imaging

Research output: Contribution to journalArticle

Related Edinburgh Organisations

Open Access permissions

Open

Documents

Original languageEnglish
Article number2169-3536
Pages (from-to)6297-6309
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 24 Dec 2018

Abstract

Super-resolution ultrasound mostly uses image-based methods for the localization of single scatterers. These methods are largely based on the centre of mass (COM) calculation. Sharpness-based localization is an alternative to COM for scatterer localization in the axial direction. Simulated ultrasound point scatterer data (centre frequency f0 = 7 MHz, wavelength λ = 220 μm) showed that the normalized sharpness method can provide scatterer axial localization with an accuracy down to 2 μm (< 0.01λ), which is a two-order of magnitude improvement compared to that achievable by conventional imaging (≈λ), and a 5-fold improvement compared to the COM estimate (≈10 μm or 0.05λ). Similar results were obtained experimentally using wire-target data acquired by the Synthetic Aperture Real-time Ultrasound System (SARUS). The performance of the proposed method was also found to be consistent across different types of ultrasound transmission. The localization precision deteriorates in the presence of noise, but even in very low signal-to-noise-ratio (SNR = 0 dB) the uncertainty was not higher than 6 μm, which outperforms the COM estimate. The method can be implemented in image data as well as using the raw signals. It is proposed that signal derived localization should replace the image-based equivalent, as it provides at least a 10 times improved accuracy.

    Research areas

  • ultrasonic imaging, Imaging, Signal to noise ratio, Standards, Signal resolution, Spatial resolution, Axial localization, center-of-mass, multiple focusing, normalized sharpness, super-resolution ultrasound

Download statistics

No data available

ID: 78405832