Raman hyperspectral imaging is a powerful method to obtain detailed chemical information about a wide variety of organic and inorganic samples noninvasively and without labels. However, due to the weak, nonresonant nature of spontaneous Raman scattering, acquiring a Raman imaging dataset is time-consuming and inefficient. In this paper we utilize a compressive imaging strategy coupled with a context-aware image prior to improve Raman imaging speed by 5- to 10-fold compared to classic point-scanning Raman imaging, while maintaining the traditional benefits of point scanning imaging, such as isotropic resolution and confocality. With faster data acquisition, large datasets can be acquired in reasonable timescales, leading to more reliable downstream analysis. On standard samples, context-aware Raman compressive imaging (CARCI) was able to reduce the number of measurements by ∼85% while maintaining high image quality (SSIM >0.85). Using CARCI, we obtained a large dataset of chemical images of fission yeast cells, showing that by collecting 5-fold more cells in a given experiment time, we were able to get more accurate chemical images, identification of rare cells, and improved biochemical modeling. For example, applying VCA to nearly 100 cells’ data together, cellular organelles were resolved that were not faithfully reconstructed by a single cell's dataset.

中文翻译：

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通过上下文感知的压缩感知进行快速共焦拉曼成像

拉曼高光谱成像是一种强大的方法，可以无创且无标记地获取有关各种有机和无机样品的详细化学信息。但是，由于自发拉曼散射的弱，非共振特性，获取拉曼成像数据集既耗时又效率低下。在本文中，我们将压缩成像策略与上下文感知图像结合使用，以使拉曼成像速度比经典点扫描拉曼成像提高5到10倍，同时保持点扫描成像的传统优势，例如各向同性的分辨率和共聚焦性。通过更快的数据采集，可以在合理的时间范围内采集大型数据集，从而使下游分析更加可靠。在标准样品上 上下文感知拉曼压缩成像（CARCI）能够将测量次数减少约85％，同时保持了高图像质量（SSIM> 0.85）。使用CARCI，我们获得了裂变酵母细胞化学图像的大型数据集，表明通过在给定的实验时间内收集5倍以上的细胞，我们能够获得更准确的化学图像，鉴定稀有细胞并改善生化模型。例如，将VCA一起应用于近100个细胞的数据，就可以解析出单个细胞的数据集无法如实重建的细胞器。我们能够获得更准确的化学图像，鉴定稀有细胞并改善生化模型。例如，将VCA一起应用于近100个细胞的数据，就可以解析出单个细胞的数据集无法如实重建的细胞器。我们能够获得更准确的化学图像，鉴定稀有细胞并改善生化模型。例如，将VCA一起应用于近100个细胞的数据，就可以解析出单个细胞的数据集无法如实重建的细胞器。