Super-Resolved Ultrasound Echo Spectra With Simultaneous Localization Using Parametric Statistical Estimation
| Autor: | Vassilis Sboros, Aris Dermitzakis, James R. Hopgood, Konstantinos Diamantis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Acoustics Bayesian inference 01 natural sciences Signal medical ultrasound microbubbles 030218 nuclear medicine & medical imaging 03 medical and health sciences symbols.namesake 0302 clinical medicine 0103 physical sciences General Materials Science 010301 acoustics Parametric statistics Physics Signal processing business.industry Echo (computing) Ultrasound General Engineering Spectral density estimation TK1-9971 Markov chain Monte Carlo Transducer Fourier transform ultrasound contrast imaging symbols Electrical engineering. Electronics. Nuclear engineering business |
| Zdroj: | IEEE Access, Vol 6, Pp 14188-14203 (2018) Diamantis, K, Dermitzakis, A, Hopgood, J & Sboros, V 2018, ' Super-resolved Ultrasound Echo Spectra with Simultaneous Localization using Parametric Statistical Estimation ', IEEE Access, vol. 6 . https://doi.org/10.1109/ACCESS.2018.2807807 |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2018.2807807 |
| Popis: | Ultrasound contrast imaging (UCI) aims to detect flow changes in the vascular bed that can help differentiate normal from diseased tissues thus providing an early screening tool for diagnosis or treatment monitoring. Ultrasound contrast agents (UCAs), used in UCI, are microbubbles that scatter ultrasound non-linearly. To date the signal processing research has successfully subtracted signals from the linear response of tissue (linear signals), but, in general, has not provided a sensitive detection that is specific to the UCA signal. This paper develops a method for the temporal and spectral estimation of linear and non-linear ultrasound echo signals. This technique is based on non-parametric methods for coarse estimation, followed by a parametric method within a Bayesian framework for estimation refinement. The results show that the pulse location can be estimated to within ±3 sample points accuracy for signals consisting of ≈80 sample points depending on the signal type, while the frequency content can be estimated to within 0.050 MHz deviations for frequencies in the 1 to 4 MHz range. This parametric spectral estimation achieved a 5-fold improvement in the frequency resolution compared with Fourier-based methods, and revealed previously unresolved frequency information that led to over 80% correct signal classification for linear and non-linear echo signals. |
| Databáze: | OpenAIRE |
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