Super-resolution spectral estimation for the analysis of non-linear signals from ultrasound scatterers

Ultrasound contrast agents are in the form of gas filled microbubbles, small enough to go through micro-circulations in human bodies. Microbubbles, when exposed to ultrasound start to oscillate under the pressure of the sound field and this oscillating, non-linear behavior results in high scattering strength. The discrimination between acoustic echoes from tissue and contrast microbubbles would have as a result the increase of the Contrast-to-Tissue-Ratio, improving therefore the quality of the imaging. The main idea is to differentiate microbubble from tissue responses based on their spectral content, as the two different kinds of signals are similar in shape but are expected to have important differences in their spectra. For this purpose, a novel parametric signal processing technique that still remains in experimental level is being investigated as it is able to estimate the frequency components of a signal with resolving capability higher than the one achieved by traditional and widely used methods until now. It is called parametric because it relies on the selection of an appropriate model and makes use of a reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to estimate the model parameters. It reduces this way the problem of spectral estimation to the simpler one of parameter estimation, providing previously unrevealed information about our data under the only condition that the selected model fits the data. The combination of better understanding of the microbubbles’ behavior together with the development of adaptive imaging techniques is very likely to improve imaging ability of this modality and allow the use of contrast agents as a therapeutic mean.