DSWE-Net: A deep learning approach for shear wave elastography and lesion segmentation using single push acoustic radiation force

Ultrasound-based non-invasive elasticity imaging modalities have received significant consideration for tissue characterization over the last few years. Though substantial advances have been made, the conventional Shear Wave Elastography (SWE) methods still suffer from poor image quality in regions far from the push location, particularly those which rely on single focused ultrasound push beam to generate shear waves. In this study, we propose DSWE-Net, a novel deep learning-based approach that is able to construct Young's modulus maps from ultrasonically tracked tissue velocity data resulting from a single acoustic radiation force (ARF) push. The proposed network employs a 3D convolutional encoder, followed by a recurrent block consisting of several Convolutional Long Short-Term Memory (ConvLSTM) layers to extract high-level spatio-temporal features from different time-frames of the input velocity data. Finally, a pair of coupled 2D convolutional decoder blocks reconstructs the modulus image and additionally performs inclusion segmentation by generating a binary mask. We also propose a multi-task learning loss function for end-to-end training of the network with 1260 data samples obtained from a simulation environment which include both bi-level and multi-level phantom structures. The performance of the proposed network is evaluated on 140 synthetic test data and the results are compared both qualitatively and quantitatively with that of the current state of the art method, Local Phase Velocity Based Imaging (LPVI). With an average SSIM of 0.90, RMSE of 0.10 and 20.69 dB PSNR, DSWE-Net performs much better on the imaging task compared to LPVI. Our method also achieves an average IoU score of 0.81 for the segmentation task which makes it suitable for localizing inclusions as well. In this initial study, we also show that our method gains an overall improvement of 0.09 in SSIM, 4.81 dB in PSNR, 2.02 dB in CNR, and 0.09 in RMSE over LPVI on a completely unseen set of CIRS tissue mimicking phantom data. This proves its better generalization capability and shows its potential for use in real-world clinical practice.