Adaptive Beamforming with Empirical Mode Decomposition

B-mode ultrasound image quality is often degraded by incoherent acoustic and electronic noise, lowering diagnostic confidence for clinicians. A number of adaptive beamforming techniques and coherence-based imaging techniques have been proposed in the past to address this problem. This paper proposes a data-driven, nonlinear technique called empirical mode decomposition (EMD), which can decompose any signal into a set of adaptive basis functions called intrinsic mode functions, to spatially filter out the undesirable noise in the per-channel domain, thereby improving the B-mode image quality. Performance of the proposed EMD-based beamforming technique is evaluated using simulation, experimental, and in vivo cardiac datasets to demonstrate proof of concept.