Physikalisch-Chemisches Institut, Ruprecht-Karls-Universität, Im Neuenheimer Feld 229, D-69120 Heidelberg, Germany.
J Biomed Opt. 2011 Feb;16(2):021105. doi: 10.1117/1.3533309.
Multiplex coherent anti-Stokes Raman scattering (MCARS) provides labeling free and fast characterization of materials and biological samples in nonlinear microscopy. In spite of its success, remaining challenges regarding the data analysis for chemoselective imaging still have to be solved. In general, image contrast has been realized by using only one spectral feature directly taken from the unprocessed raw data. This procedure is limited to strong and well separated Raman resonances like the saturated CH-stretching vibration of lipids in the case of biological samples. In order to overcome this limitation, we present a new method of MCARS data processing that exploits the whole measured spectrum to disentangle overlapping contributions of different (bio-) chemical components. Our "two-step" approach is based on the combination of imaginary part extraction followed by global fitting of the hyperspectral data set. Previous knowledge about the sample, e.g., pure spectra of the individual components is no longer necessary. The result is a highly contrasted image, where the patterns and differences between the sample components can be represented in different colors. We successfully applied this method to complex structured polymer samples and biological tissues.
多道相干反斯托克斯拉曼散射（MCARS）为非线性显微镜下的材料和生物样本提供了无标记且快速的特征描述。尽管它已经取得了成功，但在化学选择性成像的数据分析方面仍然存在一些待解决的挑战。通常，仅使用直接从未经处理的原始数据中获取的一个光谱特征来实现图像对比度。这种方法仅限于强且分离良好的拉曼共振，例如生物样本中脂质饱和 CH 伸缩振动的情况。为了克服这一限制，我们提出了一种新的 MCARS 数据处理方法，该方法利用整个测量光谱来解混不同（生物）化学组分的重叠贡献。我们的“两步”方法基于虚部提取和超光谱数据集的全局拟合相结合。不再需要关于样本的先验知识，例如单个组分的纯光谱。结果是一个对比度非常高的图像，可以用不同的颜色表示样本组分之间的图案和差异。我们成功地将该方法应用于复杂结构的聚合物样本和生物组织。
