Fast denoising and lossless spectrum extraction in stimulated Raman scattering microscopy. Issue 8 (24th May 2021)

• Record Type: Journal Article
• Authors: Li, Yanping; Shen, Binglin; Zou, Gengjin; Wang, Shiqi; Qu, Junle; Hu, Rui; Liu, Liwei
• Is Part Of: Journal of biophotonics. Volume 14:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-24
• Subjects: in vivo imaging -- Raman spectroscopy -- signal‐to‐noise ratio enhancement -- stimulated Raman scattering microscopy; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202100080
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Stimulated Raman scattering (SRS) microscopy is a nonlinear optical imaging method for visualizing chemical content based on molecular vibrational bonds. However, the imaging speed and sensitivity are currently limited by the noise of the light beam probing the Raman process. In this paper, we present a fast non‐average denoising and high‐precision Raman shift extraction method, based on a self‐reinforcing signal‐to‐noise ratio (SNR) enhancement algorithm, for SRS spectroscopy and microscopy. We compare the results of this method with the filtering methods and the reported experimental methods to demonstrate its high efficiency and high precision in spectral denoising, Raman peak extraction and image quality improvement. We demonstrate a maximum SNR enhancement of 10.3 dB in fixed tissue imaging and 11.9 dB in vivo imaging. This method reduces the cost and complexity of the SRS system and allows for high‐quality SRS imaging without use of special laser, complicated system design and Raman tags. Abstract : The imaging speed and sensitivity are limited by the noise of light beam probing the Raman process in stimulated Raman scattering (SRS) spectroscopy and microscopy. In this paper, a fast non‐average denoising and loss‐less Raman spectral extraction method based on self‐reinforcing signal‐to‐noise ratio enhancement algorithm is proposed. This method reduces the cost and complexity of SRS system and allows for high spectral and image quality improvement.