Sub-sampled Doppler ultrasound reconstruction using block sparse Bayesian learning
Duplex Doppler is an echographic mode that allows visualizing at the same time a B-mode image and the blood flow, for which a strategy for alternating velocity and B-mode emissions is required. In a recent study [1] we have shown that compressed sensing-based reconstruction of Doppler signal allowed reducing the number of Doppler emissions and yielded better results than traditional interpolation. However, in this study the reduction of Doppler emission had to be limited to 60% in order to produce satisfying reconstruction (i.e. a PSNR > 20 dB). We propose here to improve over this study by using a block sparse Bayesian learning (BSBL) framework for randomly interleaving Doppler and US emissions. The interest of using such framework in 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 for simulated data and compared to the recent results in [1]. The results show that with this method the subsampling rate can be increased up to 80% while producing satisfying reconstruction quality.