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Proceedings Paper

Adaptive coherent photoacoustic sensing
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Paper Abstract

Sensitive detection is always crucial to photoacoustic sensing and imaging applications owing to the extremely low conversion efficiency from light to sound. Conventional approach to enhance the signal-to-noise ratio (SNR) of the photoacoustic signal is data averaging, which is quite time-consuming due to multiple data acquisitions for each photoacoustic measurement. Especially for high power pulsed laser source with only 10-20 pulse repetition rate, multiple data averaging will severely degrade the frame rate. In this paper, we present a simple but efficient way, called adaptive coherent photoacoustic (aCPA) sensing to obviously enhance the detected signal SNR with only single laser pulse. More specifically, The proposed aCPA employs an adaptive matched filter to cross-correlate with the raw time-domain PA signal iteratively. The optimum matched filter could be found after several iterations, leading to improved signal SNR. In vivo experimental results show that the proposed aCPA method improved the signal SNR by about 60 dB with single PA measurement. In conventional data averaging, 106 times PA measurements is required to achieve same SNR improvement. In other words, sensing and imaging speed is improved by 106 times in theory. It demonstrates the potential of aCPA to perform highly sensitive photoacoustic sensing and imaging with significantly accelerated speed.

Paper Details

Date Published: 19 February 2018
PDF: 6 pages
Proc. SPIE 10494, Photons Plus Ultrasound: Imaging and Sensing 2018, 104946G (19 February 2018); doi: 10.1117/12.2292627
Show Author Affiliations
Fei Gao, ShanghaiTech Univ. (China)
Nanyang Technological Univ. (Singapore)
Xiaohua Feng, Nanyang Technological Univ. (Singapore)
Ruochong Zhang, Nanyang Technological Univ. (Singapore)
Siyu Liu, Nanyang Technological Univ. (Singapore)
Yuanjin Zheng, Nanyang Technological Univ. (Singapore)


Published in SPIE Proceedings Vol. 10494:
Photons Plus Ultrasound: Imaging and Sensing 2018
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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