Beamforming and Speckle Reduction Using Neural Networks

Abstract

With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In this paper, we present a framework for using neural networks to beamform ultrasound channel signals into speckle-reduced B-mode images. We introduce log-domain normalization-independent loss functions that are appropriate for ultrasound imaging. A fully convolutional neural network was trained with simulated channel signals that were co-registered spatially to ground truth maps of echogenicity. Networks were designed to accept 16 beamformed subaperture radiofrequency signals. Training performance was compared as a function of training objective, network depth, and network width. The networks were then evaluated on simulation, phantom, and in vivo data and compared against existing speckle reduction techniques. The most effective configuration was found to be the deepest (16 layer) and widest (32 filter) networks, trained to minimize a normalization-independent mixture of the ℓ 1 and multi-scale structural similarity losses. The neural network significantly outperformed delay-and-sum and receive-only spatial compounding in speckle reduction while preserving resolution and exhibited improved detail preservation over a non-local means methods. This work demonstrates that ultrasound B-mode image reconstruction using machine-learned neural networks is feasible and establishes that networks trained solely in silico can be generalized to real-world imaging in vivo to produce images with significantly reduced speckle.