Beamforming with sparse prior in ultrasound medical imaging

Abstract

Nowadays the classical Delay-and-Sum (DAS) beamformer is extensively used in ultrasound imaging due to its low computational characteristics. However, it suffers from high sidelobe level, poor resolution and low contrast. An alternative is the Minimum-Variance (MV) beamformer which results in a higher image quality both in terms of spatial resolution and contrast. Even so, these benefits come at the expense of a higher computation complexity that limits its real-time capabilities. One solution that recently gained noticeable interest is the exploit of the sparsity of the scanned medium. Based on this assumption, we extend the DAS method to yield sparse results by using the Bayesian Information Criterion (BIC). Our realistic simulations demonstrate that the proposed beamforming (BF) method shows better performance than the classical DAS and MV in terms of lateral resolution, sidelobe reduction and contrast.