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

Daohuai Jiang; Hengrong Lan; Fei Gao

doi:10.1117/12.2578088

5 March 2021 Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel

Daohuai Jiang, Hengrong Lan, Fei Gao

Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 116422E (2021) https://doi.org/10.1117/12.2578088
Event: SPIE BiOS, 2021, Online Only

Abstract

Photoacoustic tomography (PAT) is an emerging technology for biomedical imaging that combines the superiorities of high optical contrast and acoustic penetration. In the PAT system, more photoacoustic (PA) signals are preferred to be detected to reconstruct PA image with higher fidelity. However, more PA signals’ detection leads to more time consumption for single-channel scanning based PAT system, or higher cost of data acquisition (DAQ) module for array-based PAT system. To address this issue, we proposed a real-time PAT system only using single DAQ channel, and a deep learning method for PA signal recover and image reconstruction. We superimpose 30 channels’ signals together, shrinking to 4 channels (120/30=4). Furthermore, a four-to-one delay-line module is designed to combine this 4 channels’ data into one channel DAQ. In order to reconstruct the image from four superimposed 30-channels’ PA signals, we train a dedicated deep learning model to reconstruct final PA image.

Conference Presentation

Citation Download Citation

Daohuai Jiang, Hengrong Lan, and Fei Gao "Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 116422E (5 March 2021); https://doi.org/10.1117/12.2578088

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