Hybrid Imaging System Laboratory, School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, Shanghai, China.
J Biophotonics. 2024 Feb;17(2):e202300289. doi: 10.1002/jbio.202300289. Epub 2023 Dec 12.
Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor 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 gradient-based adaptive wavelet de-noising method, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex-vivo and in-vivo experiments using acoustic-resolution PAM. The quality of de-noised PA image/signal by our proposed algorithm has improved by at least 30%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. Moreover, it produces good results when dealing with multi-layer structures. 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.
过去几十年来，光声成像（PAI）已经应用于许多生物医学应用中。然而，接收到的 PA 信号通常会受到 SNR 差的影响。传统的解决方案是使用更高功率的激光，或进行长时间的信号平均，这可能会增加系统成本、时间消耗和组织损伤。另一种策略是设计去噪算法。在本文中，我们提出了一种基于梯度的自适应小波去噪方法，该方法将低频小波分量的能量梯度突变点设置为阈值。我们使用声分辨率 PAM 进行了模拟、离体和体内实验。与传统的信号去噪算法相比，我们提出的算法至少将去噪后的 PA 图像/信号的质量提高了 30%，这可以产生更好的对比度和更清晰的细节。此外，它在处理多层结构时也能产生良好的效果。该去噪方法为在单次低功率激光照射下提高生物医学应用中 PA 信号的 SNR 提供了潜力。
