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受激拉曼散射显微镜的组织成像深度限制

Tissue imaging depth limit of stimulated Raman scattering microscopy.

作者信息

Hill Andrew H, Manifold Bryce, Fu Dan

机构信息

Department of Chemistry, University of Washington, Seattle, Washington 98195, USA.

These authors contributed equally to the preparation of this manuscript.

出版信息

Biomed Opt Express. 2020 Jan 13;11(2):762-774. doi: 10.1364/BOE.382396. eCollection 2020 Feb 1.

Abstract

Stimulated Raman scattering (SRS) microscopy is a promising technique for studying tissue structure, physiology, and function. Similar to other nonlinear optical imaging techniques, SRS is severely limited in imaging depth due to the turbidity and heterogeneity of tissue, regardless of whether imaging in the transmissive or epi mode. While this challenge is well known, important imaging parameters (namely maximum imaging depth and imaging signal to noise ratio) have rarely been reported in the literature. It is also important to compare epi mode and transmissive mode imaging to determine the best geometry for many tissue imaging applications. In this manuscript we report the achievable signal sizes and imaging depths using a simultaneous epi/transmissive imaging approach in four different murine tissues; brain, lung, kidney, and liver. For all four cases we report maximum signal sizes, scattering lengths, and achievable imaging depths as a function of tissue type and sample thickness. We report that for murine brain samples thinner than 2 mm transmissive imaging provides better results, while samples 2 mm and thicker are best imaged with epi imaging. We also demonstrate the use of a CNN-based denoising algorithm to yield a 40 µm (24%) increase in achievable imaging depth.

摘要

受激拉曼散射(SRS)显微镜是一种用于研究组织结构、生理学和功能的有前景的技术。与其他非线性光学成像技术类似,由于组织的浑浊度和异质性,无论在透射模式还是反射模式下成像,SRS的成像深度都受到严重限制。虽然这一挑战是众所周知的,但重要的成像参数(即最大成像深度和成像信噪比)在文献中很少被报道。比较反射模式和透射模式成像以确定许多组织成像应用的最佳几何结构也很重要。在本手稿中,我们报告了使用同时反射/透射成像方法在四种不同的小鼠组织(脑、肺、肾和肝)中可实现的信号大小和成像深度。对于所有四种情况,我们报告了最大信号大小、散射长度以及可实现的成像深度作为组织类型和样品厚度的函数。我们报告称,对于厚度小于2毫米的小鼠脑样本,透射成像提供了更好的结果,而厚度为2毫米及以上的样本采用反射成像最佳。我们还展示了使用基于卷积神经网络的去噪算法可使可实现的成像深度增加40微米(24%)。

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