Abstract

The understanding and exploitation of acoustic echo signals from nonlinear ultrasound scatterers is an active research area that aims to improve the sensitivity and specificity of diagnostic imaging. Discriminating between acoustic echoes from linear scatterers, such as tissue, and nonlinear scatterers, such as contrast microbubbles, based on their frequency content is also an important topic in ultrasound contrast imaging. In order to achieve these objectives, a fundamental preliminary stage is to extract information about the reflected signals in the frequency domain with high accuracy: this is essentially a feature extraction and estimation problem. In this paper, a parametric Bayesian spectral estimation method is utilised for the analysis of the backscattered echo signals from microbubbles. In contrast to existing nonparametric discrete-Fourier-transform- (DFT-) based spectral estimation techniques used in the ultrasonic literature, this method is able to estimate the number of spectral components as well as their amplitudes and frequencies. The Bayesian spectral analysis technique has improved frequency resolution compared with the DFT for shortmultiple-component signals at low signal-to-noise ratios. The performance of the method is demonstrated with simulated signals, as well as analysing experimentally measured echo signals from nonlinear microbubble scatterers.

Highlights

  • Ultrasound contrast agents (UCAs) used for enhancing ultrasound images were first discovered accidentally by cardiologist Charles Joiner in the 1960’s [1]

  • The frequency resolution using Bayesian spectral estimation in a parametric framework mainly depends on the signal-tonoise ratio (SNR) of a signal, in addition to the signal duration in samples [12]

  • This paper introduces Bayesian spectral estimation for the analysis of echo signals from ultrasound contrast MBs

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Summary

Introduction

Ultrasound contrast agents (UCAs) used for enhancing ultrasound images were first discovered accidentally by cardiologist Charles Joiner in the 1960’s [1]. Using Bayesian inference, a posterior PDF, p(k, Φk | y), for the pulse parameters, Φk, can be derived, where y is the observed data and k is the number of sinusoids in the signal pulse This PDF encapsulates all the information needed to characterise the ultrasound echo signal, it is still necessary to find a point estimate for the “optimal” parameter set. The improvements provided by the Bayesian approach compared with classical analysis are significant This algorithm is used to analyse the spectral characteristics of pulse echo returns from nonlinearly scattering MBs [3,4,5].

Spectral Analysis in UCI
Parametric Bayesian Spectral Analysis
Results
Discussion and Conclusions
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