Super-Resolved Ultrasound Echo Spectra With Simultaneous Localization Using Parametric Statistical Estimation

Abstract

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.

Keywords

• Bayesian inference
• Markov chain Monte Carlo
• medical ultrasound
• microbubbles
• ultrasound contrast imaging