Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm
Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm
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DOI：
10.1364/ol.480667
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发表时间：
2023-02-01
期刊：
影响因子：
3.6
通讯作者：
Wei, Haoyun
中科院分区：
文献类型：
--
作者：
Zhang, Yujia;Lu, Minjian;Wei, Haoyun
快速相干拉曼高光谱成像在传感、医疗诊断和动态代谢监测方面的应用显示出巨大的前景。然而，当前多重相干反斯托克斯拉曼散射（CARS）显微镜的光谱采集速度普遍受到光谱仪积分时间的限制，并且随着检测速度的提高，单光谱的信噪比（SNR）会下降，导致成像质量较差。在这封信中，我们报告了一种通过集成快速延迟光谱聚焦方法和深度学习两种方法开发的双梳相干拉曼高光谱显微镜成像系统。通过将相对延迟扫描聚焦在有效拉曼激发区域，利用光谱刷新率，实现36 kHz的光谱采集速度，近似于4帧/秒，像素分辨率为95 x 95像素，光谱带宽不小于200 cm(-1)。为了提高光谱信噪比和成像质量，深度学习模型被设计用于光谱预处理和自动无监督特征提取。此外，通过改变梳对的相对延迟聚焦区域，可以灵活地将检测到的光谱波数区域调谐到光谱的高SNR区域。 (c) 2023 Optica 出版集团
Rapid coherent Raman hyperspectral imaging shows great promise for applications in sensing, medical diagnostics, and dynamic metabolism monitoring. However, the spectral acquisition speed of current multiplex coherent anti-Stokes Raman scattering (CARS) microscopy is generally limited by the spectrometer integration time, and as the detection speed increases, the signal-to-noise ratio (SNR) of single spectrum will decrease, leading to a terrible imaging quality. In this Letter, we report a dual-comb coherent Raman hyperspec-tral microscopy imaging system developed by integrating two approaches, a rapid delay-spectral focusing method and deep learning. The spectral refresh rate is exploited by focusing the relative delay scanning in the effective Raman excitation region, enabling a spectral acquisition speed of 36 kHz, approximate to 4 frames/s, for a pixel resolution of 95 x 95 pixels and a spectral bandwidth no less than 200 cm(-1). To improve the spectral SNR and imaging quality, the deep learning models are designed for spectral preprocessing and automatic unsupervised feature extraction. In addition, by changing the relative delay focusing region of the comb pairs, the detected spectral wavenumber region can be flexibly tuned to the high SNR region of the spectrum. (c) 2023 Optica Publishing Group