SHEAR-net: An End-to-End Deep Learning Approach for Single Push Ultrasound Shear Wave Elasticity Imaging

Ultrasound Shear Wave Elastography (USWE) with conventional B-mode imaging demonstrates better performance in lesion segmentation and classification problems. In this article, we propose SHEAR-net, an end-to-end deep neural network, to reconstruct USWE images from tracked tissue displacement data at different time instants induced by a single acoustic radiation force (ARF) with 100% or 50% of the energy in conventional use. The SHEAR-net consists of a localizer called the S-net to first localize the lesion location and then uses recurrent layers to extract temporal correlations from wave patterns using different time frames, and finally, with an estimator, it reconstructs the shear modulus image from the concatenated outputs of S-net and recurrent layers. The network is trained with 800 simulation and a limited number of CIRS tissue mimicking phantom data and is optimized using a multi-task learning loss function where the tasks are: inclusion localization and modulus estimation. The efficacy of the proposed SHEAR-net is extensively evaluated both qualitatively and quantitatively on 125 test set of motion data obtained from simulation and CIRS phantoms. We show that the proposed approach consistently outperforms the current state-of-the-art method and achieves overall 4-5 dB improvement in PSNR and SNR. In addition, an average gain of 0.15 in DSC and SSIM values indicate that the SHEAR-net has a better inclusion coverage area and structural similarity of the two approaches. The proposed real-time deep learning based technique can accurately estimate shear modulus for a minimum tissue displacement of 0.5$\mu$m and image multiple inclusions with a single push ARF.