PROJECT TITLE :

Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning

ABSTRACT:

In this paper we have a tendency to propose a framework for using duplex Doppler ultrasound systems. These type of systems want to interleave the acquisition and display of a B-mode image and of the pulsed Doppler spectrogram. In an exceedingly recent study (Richy , 2013), we tend to have shown that compressed sensing-based mostly reconstruction of Doppler signal allowed reducing the amount of Doppler emissions and yielded higher results than traditional interpolation and at least equivalent or maybe higher depending on the configuration than the study estimating the signal from sparse information sets given in Jensen, 2006. We have a tendency to propose here to enhance over this study by employing a novel framework for randomly interleaving Doppler and US emissions. The proposed methodology reconstructs the Doppler signal section by phase employing a block sparse Bayesian learning (BSBL) algorithm primarily based CS reconstruction. The interest of using such framework within the context of duplex Doppler is linked to the unique ability of BSBL to exploit block-correlated signals and to recover non-sparse signals. The performance of the technique is evaluated from simulated data with experimental in vivo information and compared to the recent ends up in Richy , 2013.