Quantitative Imaging Network for Versatile Ultrasound Tomography

Abstract

In this paper, a limited-angle Ultrasound Computed Tomography (USCT) system capturing quantitative features is presented. Quantitative characteristics of tissues such as speed of sound (SS) and attenuation coefficient (AC), have great potential to distinguish malignant and benign tissues. The proposed system requires two facing linear array transducers to measure the time of flight and the amplitude of traversed waves. The Quantitative Imaging Network (QI-Net) is modeled and trained for stable image reconstruction from ultrasonic information achieved from two facing limited-angle probes. In addition, a Quantitative Imaging Network incorporating a priori information (QIP-Net) to the neural network is also presented. A robust ROI compression scheme embedded in the proposed networks extracts quantitative image information regardless of the measurement size. We evaluated our methods via numerical simulation, phantom, and ex-vivo measurements. The simulation results show that the QI-Net and QIP-Net are capable of quantifying SS with the average error of 1.1m/s (0.56%) and 4.5m/s (2.3%), respectively. In the phantom and ex-vivo studies, the networks demonstrate accurate extraction of SS and AC under diverse conditions.