Elastography mapped by deep convolutional neural networks

Abstract

Elastography emerges as a medical modality to map stiffness distribution of tissues and is expected to help identify malignant tumors. To this end, tissues are externally stimulated with dynamic waves, and thereafter mechanical responses are internally measured. However, internal measurements limit the resolution and accuracy due to wave scattering and frequency-dependence. Although models have been reported only with need for acquiring transmitted responses, the computational processes are timeconsuming in the inverse analysis. Here we develop an architecture of deep learning-based convolutional neural networks (CNNs) to image elastography based on sound transmission. The proposed CNNs contain three branches, one of which considers the contribution of original features in input data. By comparison, the developed architecture not only maps elastography accurately, but also is more efficient than traditional CNNs in sequence.