Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the speed of a laterally-travelling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using side-by-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.8 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through t-Distributed Stochastic Neighbor Embedding reveals a high degree of overlap between data from different scanners. Also qualitatively, sSWE results seem generalisable to single B-mode acquisitions and other scanners. In the future, we envision sSWE as a reliable elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition.