A Learned Representation For Multi-Variable Ultrasonic Lesion Quantification

Abstract

In this paper, a single-probe ultrasonic imaging system that captures multi-variable quantitative profiles is presented. As pathological changes cause biomechanical property variation, quantitative imaging has great potential for lesion characterization. The proposed system simultaneously extracts four clinically informative quantitative biomarkers, such as the speed of sound, attenuation, effective scatter density, and effective scatter radius, in real-time using a single scalable neural network. The performance of the proposed system was evaluated through numerical simulations, and phantom and ex vivo measurements. The simulation results demonstrated that the proposed SQI-Net reconstructs four quantitative images with PSNR and SSIM of 19.52 dB and 0.8251, respectively, while achieving a parameter reduction of 75% compared to the design of four parallel networks, each of which was dedicated to a single parameter. In the phantom and ex vivo experiments, the SQI-Net demonstrated the classification of cyst, and benign- and malignant-like inclusions through a comprehensive analysis of four reconstructed images.