Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, China.
Analyst. 2021 Apr 7;146(7):2348-2357. doi: 10.1039/d1an00088h. Epub 2021 Feb 24.
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.
拉曼高光谱成像是一种强大的方法，可以非侵入性地、无需标记地获取各种有机和无机样品的详细化学信息。然而，由于自发拉曼散射的微弱、非共振性质，获取拉曼成像数据集是耗时且低效的。在本文中，我们利用压缩成像策略结合上下文感知图像先验，与经典的点扫描拉曼成像相比，将拉曼成像速度提高了 5 到 10 倍，同时保持了点扫描成像的传统优势，如各向同性分辨率和共聚焦性。更快的数据采集可以在合理的时间内获取大型数据集，从而实现更可靠的下游分析。在标准样本上，上下文感知拉曼压缩成像 (CARCI) 能够将测量次数减少约 85%，同时保持高图像质量 (SSIM>0.85)。使用 CARCI，我们获得了大量裂殖酵母细胞的化学图像数据集，表明在给定的实验时间内，通过收集 5 倍以上的细胞，我们能够获得更准确的化学图像、识别罕见细胞和改进生化建模。例如，将 VCA 应用于近 100 个细胞的数据中，可以解析出单个细胞数据集无法准确重建的细胞细胞器。
