Denoising method for photoacoustic microscopy using deep learning

Kanggao Tang; Bo Li; Jinhong Zhang; Jianshuang Wei; Lingfang Song; Xianlin Song

doi:10.1117/12.2584879

8 November 2020 Denoising method for photoacoustic microscopy using deep learning

Kanggao Tang, Bo Li, Jinhong Zhang, Jianshuang Wei, Lingfang Song, Xianlin Song

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115252P (2020) https://doi.org/10.1117/12.2584879
Event: SPIE Future Sensing Technologies, 2020, Online Only

Conference Poster

Abstract

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

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Kanggao Tang, Bo Li, Jinhong Zhang, Jianshuang Wei, Lingfang Song, and Xianlin Song "Denoising method for photoacoustic microscopy using deep learning", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115252P (8 November 2020); https://doi.org/10.1117/12.2584879

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