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Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo
  • +2
  • Xinke Li,
  • Peng Ge,
  • Yuting Shen,
  • Feng Gao,
  • Fei Gao
Xinke Li
ShanghaiTech University Hybrid Imaging System Laboratory

Corresponding Author:lixk@shanghaitech.edu.cn

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Peng Ge
ShanghaiTech University Hybrid Imaging System Laboratory
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Yuting Shen
ShanghaiTech University Hybrid Imaging System Laboratory
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Feng Gao
ShanghaiTech University Hybrid Imaging System Laboratory
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Fei Gao
ShanghaiTech University Hybrid Imaging System Laboratory
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Abstract

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor signal-to-noise ratio (SNR). Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy is de-noising algorithm design. In this paper, we propose a new de-noising method, termed gradient-based adaptive wavelet de-noising, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex vivo and in vivo experiments to validate the performance of the algorithm. The quality of de-noised PA image/signal by our proposed algorithm has improved by 20%-40%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.
31 Jul 2023Submitted to Journal of Biophotonics
31 Jul 2023Submission Checks Completed
31 Jul 2023Assigned to Editor
31 Jul 2023Review(s) Completed, Editorial Evaluation Pending
31 Jul 2023Reviewer(s) Assigned
21 Aug 2023Editorial Decision: Revise Major
12 Sep 20231st Revision Received
13 Sep 2023Submission Checks Completed
13 Sep 2023Assigned to Editor
13 Sep 2023Reviewer(s) Assigned
13 Sep 2023Review(s) Completed, Editorial Evaluation Pending
07 Nov 2023Editorial Decision: Accept