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Raman spectroscopic imaging (RSI) can simultaneously measure the spectra and spatial information of samples. The measured data include two-dimensional spatial images and one-dimensional Raman spectra. Traditionally, RSI can be obtained by point scanning, line scanning, and wide-field illumination schemes. The point scanning scheme provides the best resolution but a lower imaging speed. On the contrary, wide-field illumination can image fast but provides a lower spatial resolution. To integrate the advantages of the two schemes, a complementary scheme for wide-field RSI was proposed, which uses frequency modulation based spatially encoded (FMSE) light as the excitation. In this scheme, all the beams of wide-field illumination are modulated at different frequencies. Thus, each excitation beam has its own modulation frequency, and the excited Raman signal will carry this modulation information. For the detection module, a single point detector was used to collect time series Raman signals carrying the unique modulation information. Using a sparse reconstruction-based demodulation strategy, the Raman image can be recovered effectively. The feasibility of the method was verified with numerical simulations. The reconstruction results showed that it is feasible to conduct Raman spectroscopic imaging with high-resolution and high speed under the illumination of FMSE light and detection of a single-point detector. [ABSTRACT FROM AUTHOR]

Compared with static optical imaging, dynamic optical imaging technology can obtain quantitative pharmacokinetics information, such as the probe metabolism curve, removal rate, and binding potential of the receptor. Accurate segmentation of the region of interest (ROI) is an important step in dynamic optical imaging. Generally, the ROI is manually labeled by researchers based on experience. This will lead to two unavoidable problems. First, manual segmentation of the ROI is very time consuming, especially when there are many sequential dynamic optical images. Second, manual segmentation cannot ensure accuracy when the optical signal gradually decays to a point at which it is difficult to distinguish by using the naked eyes. These problems will inevitably lead to inaccuracy of quantitative results of dynamic optical imaging. Here, we presented a machine learning-based automatic segmentation method to avoid these time-consuming and inaccuracy problems caused by manual segmentation. The K-means clustering algorithm and fuzzy c-means clustering algorithm were implemented to separate the ROI from the background of sequential dynamic optical images. Automatic selection of clustering results was completed by mathematical methods. The accuracy and feasibility of machine learning-based methods were verified by comparing their results with the manual segmentation results. The preliminary results demonstrated that the machine learning-based automatic segmentation has coherent performance with the manual one. [ABSTRACT FROM AUTHOR]