  Ultrasound Shear Wave Elastography (SWE) is a noteworthy tool for in-vivo
noninvasive tissue pathology assessment. State-of-the-art techniques can
generate reasonable estimates of tissue elasticity, but high-quality and
noise-resiliency in SWE reconstruction have yet to demonstrate advancements. In
this work, we propose a two-stage DL pipeline producing reliable
reconstructions and denoise said reconstructions to obtain lower noise
prevailing elasticity mappings. The reconstruction network consists of a
Resnet3D Encoder to extract temporal context from the sequential multi-push
data. The encoded features are sent to multiple Nested CNN LSTMs which process
them in a temporal attention-guided windowing basis and map the 3D features to
2D using FFT-attention, which are then decoded into an elasticity map as
primary reconstruction. The 2D maps from each multi-push region are merged and
cleaned using a dual-decoder denoiser network, which independently denoises
foreground and background before fusion. The post-denoiser generates a
higher-quality reconstruction and an inclusion-segmentation mask. A
multi-objective loss is designed to accommodate the denoising, fusing, and
segmentation processes. The method is validated on sequential multi-push SWE
motion data with multiple overlapping regions. A patch-based training procedure
is introduced with network modifications to handle data scarcity. Evaluations
produce 32.66dB PSNR, 43.19dB CNR in noisy simulation, and 22.44dB PSNR,
36.88dB CNR in experimental data, across all test samples. Moreover, IoUs
(0.909 and 0.781) were quite satisfactory in the datasets. After comparing with
other reported deep-learning approaches, our method proves quantitatively and
qualitatively superior in dealing with noise influences in SWE data. From a
performance point of view, our deep-learning pipeline has the potential to
become utilitarian in the clinical domain.
