Development of Super-Resolution Sharpness-Based Axial Localization for Ultrasound Imaging

Abstract

Super-resolution ultrasound mostly uses image-based methods for the localization of single scatterers. These methods are largely based on the center of mass (COM) calculation. Sharpness-based localization is an alternative to COM for scatterer localization in the axial direction. Simulated ultrasound point scatterer data (center frequency f 0 = 7 MHz and 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 the conventional imaging (≈λ), and a five-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. 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-tonoise 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 by using the raw signals. It is proposed that the signal-derived localization should replace the image-based equivalent, as it provides at least 10 times improved accuracy. INDEX TERMS Axial localization, center of mass, multiple focusing, normalized sharpness, superresolution ultrasound.

A Novel Relative Position Estimation Method for Capsule Robot Moving in Gastrointestinal Tract

“…It is not suitable for narrow environments, such as in the gastrointestinal tract. Ultrasound-based methods [14,15] have been widely used in the biomedical area, but their accuracy is limited due to speed-of-sound variations in human tissue. Medical imaging methods [16,17] have used images from the endoscope to do localizations, but they depend on the accuracy of the sample before the test, and the matching of image features is difficult to achieve.…”