Instantaneous ultrasound computed tomography using deep convolutional neural networks

Robert W. Donaldson; Jiaze He

doi:10.1117/12.2582630

22 March 2021 Instantaneous ultrasound computed tomography using deep convolutional neural networks

Robert W. Donaldson, Jiaze He

Proceedings Volume 11593, Health Monitoring of Structural and Biological Systems XV; 1159325 (2021) https://doi.org/10.1117/12.2582630
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Abstract

Ultrasound computed tomography (USCT) receives increasing attention because of its capability to reconstruct quantitative information about the material property distribution as images with superior resolution. However, one roadblock for the wide adoption of relevant techniques is the high demand for computational resources and the long processing time for solving a large inverse problem in imaging. To alleviate the associated challenges, a two-stage inversion scheme is proposed: 1) the ultrasound scanning signals are first processed using a full waveform inversion (FWI) technique with a single iteration to rapidly create a model (image) with embedded wave speed distribution; 2) the corresponding image will be further improved by feeding into a pre-trained deep neural network. The deep learning models presented in this paper are built upon two architectures to instantaneously solve the associated inverse problems and to produce a high-resolution image in real-time. The first is based on 1D convolutional neural network (1D-CNN) layers with an autoencoder structure. The second implements additional layers and skip connections inspired by a U-Net architecture. The resultant superior reconstructions from both CNNs demonstrate that the proposed framework produces a high-resolution image from a rapidly-generated, low-resolution image in real-time, with dramatically improved results.

Conference Presentation

Citation Download Citation

Robert W. Donaldson and Jiaze He "Instantaneous ultrasound computed tomography using deep convolutional neural networks", Proc. SPIE 11593, Health Monitoring of Structural and Biological Systems XV, 1159325 (22 March 2021); https://doi.org/10.1117/12.2582630

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