| Περίληψη: | In ultrasound contrast imaging, 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 the responses from those two kinds of signals based on their spectral content. The most important features of those sinusoidal signals are that they are very short in duration and than they are very likely to have many closely spaced frequency components. So, in order to achieve this target a novel Bayesian parametric spectral estimation technique has been originally designed by Yan Yan (PhD University of Edinburgh), that is supposed to have greater resolving capabilities than commonly used spectral estimation methods. The new technique uses a reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm so as to identify the frequency components of a signal and it is called parametric because it assumes a model and then the problem of spectral estimation is reduced to that of estimating the parameters of the model.
This new method has been initially tested with synthetic signals created in Matlab, so as to define on which parameters it depends and to extract mathematical equations that describe these dependences. And although some coarse comparisons with other techniques showed that the capabilities of this method were great, there was plenty room for improvements. Corrections in the Matlab code of this method, analysis of the code’s output in various ways so as to find which is superior, and the proposal of a new simpler model are just some of the changes that have evidently improved the method’s function. But the most important one is the completion of the amplitude estimation that was left unfinished in the past, as a complete spectral analysis implies both frequency and amplitude estimation. Now, signal reconstruction is possible and also, direct comparisons of the method’s resulting spectrum with the one of the Discrete Fourier Transform or of any other nonparametric (DFT-based) or parametric method can be made. The new version of the code has been applied apart from synthetic signals, to the real ones providing indeed information that was undisclosed in the past concerning the spectral content of those signals. However, further research is required, in order to take advantage of this information and in order to determine the exact performance and limitations of this method that remains still in experimental level.
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