拉曼成像是一种无损伤、 无需标记的光谱成像技术, 在生物医学领域得到了广泛的应用。 然而, 由于大多数生物样本中的自发拉曼信号都很弱, 为了获得较好的成像结果, 需要较长的时间来获取高信噪比的拉曼光谱, 严重影响了拉曼成像的时空分辨率, 阻碍了其在快速动态体系中的应用。 多通道拉曼成像是解决这一问题的有效途径之一, 在多通道拉曼成像技术中, 完整拉曼光谱的标定-重建算法是关键。 目前, 适用于光谱重建的算法有伪逆法、 Wiener估计算法等, 这些方法虽然简单且易于实现, 但是在应用于多通道拉曼成像时, 一方面易受噪声、 振动等非线性因素的直接干扰, 另一方面在多通道拉曼成像中, 数量相对较少的训练样本和坏样本的存在均很容易影响重建效果。 为解决这两类因素的影响, 本文提出了一种基于训练样本优化和主成分分析(PCA)的拉曼光谱重建算法。 首先, 利用滤光片理论响应矩阵函数计算训练样本的模拟窄带测量值, 借助Wiener估计重建完整拉曼光谱, 得到重建光谱的模拟窄带测量值, 比较样本与重建光谱的窄带测量值, 完成训练样本的优化; 然后, 基于多项式回归, 拓展优化处理后的窄带测量值, 降低非线性因素的干扰; 最后, 利用主成分分析, 提取训练样本主要信息, 完成转移矩阵的计算, 并引入归一化处理, 实现拉曼光谱的快速重建。 在试验中, 选取有机玻璃(PMMA)作为实验样本, 利用伪逆法、 Wiener估计算法和本算法, 分别完成拉曼光谱重建。 采用均方根误差, 评价拉曼光谱的重建精度。 结果证明, 该算法优于传统算法, 为拉曼成像技术进一步在快速动态体系中的应用提供了理论支持。

Raman imaging is a noninvasive, label-free spectral imaging technique that has been widely used in the biomedical field. However, the spontaneous Raman signals of most biological samples are weak. It takes a long time to obtain an image with a high signal-to-noise ratio, which seriously affects the spatial and temporal resolution of Raman imaging and hinders its application in fast dynamic systems. The multi-channel Raman imaging is one of the effective ways to solve this problem. The reconstruction of the full Raman spectrum is the key in this system, and the corresponding algorithm of reconstruction is needed to be developed. At present, the algorithms capable for spectral reconstruction are pseudo-inverse and Wiener estimation. Although these methods are simple and easy to be carried out, they are susceptible to nonlinear factors such as noise and vibration when applied to the system of multi-channel. On the other hand, the numbers of the training sample are relatively small, and the bad sample affects the reconstruction in the system of multi-channel. In order to solve those problems, we propose an algorithm based on sample optimization and principal component analysis (PCA). Firstly, the simulated narrow-band measurements of the training samples are calculated by using the spectral response function of the filter, and the full Raman spectra are reconstructed by Wiener estimation, and then the simulated narrow-band measurements of the reconstructed spectra are achieved. The sample gets Optimized by comparing the simulated narrow-band measurements of the sample and the reconstructed spectra. Second, the interference of nonlinear factors is reduced by introducing the polynomial regression and expanding the optimized narrow-band measurements. At last, the main information of training samples is extracted, and the calculation is reduced by using PCA, and the transform matrix is completed. At the same time, the normalization is introduced to realize the reconstruction of Raman spectra. In the experiment, the polymethyl methacrylate is selected as the experimental sample, and the Raman spectrum is reconstructed by pseudo-inverse, Wiener estimation and our algorithm. The root means square error is used to evaluate the accuracy of the reconstructed spectra. The result proves that our algorithm is significant. It provides theoretical support for the further application of Raman imaging technology in fast dynamic systems.