Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel

Authors: Hengrong Lana,b,c,1, Daohuai Jianga,b,c,1, Feng Gaoa, Fei Gaoa,*

Affiliations

a Hybrid Imaging System Laboratory, Shanghai Engineering Research Center of Intelligent Vision and Imaging, School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

b Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information Technology, Shanghai, 200050, China

c University of Chinese Academy of Sciences, Beijing, 100049, China

Abstract

Photoacoustic computed tomography (PACT) combines the optical contrast of optical imaging and the penetrability of sonography. In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel. To reduce the channel number of DAQ, we superimpose 30 nearby channels’ signals together in the analog domain, and shrinking to 4 channels of data (120/30 = 4). Furthermore, a four-to-one delay-line module is designed to combine these four channels’ data into one channel before entering the single-channel DAQ, followed by decoupling the signals after data acquisition. To reconstruct the image from four superimposed 30-channels’ PA signals, we train a dedicated deep learning model to reconstruct the final PA image. In this paper, we present the preliminary results of phantom and in-vivo experiments, which manifests its robust real-time imaging performance. The significance of this novel PACT system is that it dramatically reduces the cost of multi-channel DAQ
module (from 120 channels to 1 channel), paving the way to a portable, low-cost and real-time PACT system.

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