MATLAB PROJECT
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 the
simulated channel signals that were coregistered spatially to ground-truth maps
of echogenicity. Networks were designed to accept 16 beamformed subaperture
radio frequency (RF) signals. Training performance was compared as a function
of training objective, network depth, and network width. The networks were then
evaluated on the simulation, phantom, and in vivo data and compared against the
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
multiscale structural similarity (MS-SSIM) losses. The neural network
significantly outperformed delayand-sum (DAS) and receive-only spatial
compounding in speckle reduction while preserving resolution and exhibited
improved detail preservation over a nonlocal means method. 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
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