Raman spectroscopic imaging can provide three-dimensional data set of samples, including two-dimensional spatial image and one-dimensional Raman spectral data. Currently, three strategies can be used to achieve Raman spectroscopic imaging, including point scanning, line scanning, and wide-field illumination. Point scanning method provides the best resolution but has low imaging speed. On the contrary, wide-field illumination can image fast but provides lower spatial resolution. To integrate the advantages of two methods, a new strategy for large-field Raman spectroscopic imaging was proposed, which uses the frequency modulation based spatially encoded light as the excitation. In this method, millions of single beams simultaneously illuminate on the sample to act as the wide-field illumination. Each beam illuminates on different positions of the sample, whose intensity are modulated with different frequencies. Thus, each excitation beam has its own modulation frequency and the excited Raman signal will carry the modulation information. At the detection end, a single point detector was used to collect the time series Raman signals carrying the unique modulation information. Using the sparse reconstruction based on demodulation strategy, the Raman image can be recovered effectively. The feasibility of the method was verified with numerical simulations. The results showed that it is feasible to conduct Raman spectroscopic imaging with high-resolution and high speed under the illumination of frequency modulation based spatially encoded light and the detection of single-point detector.
Raman tomography can provide quantitative distribution of chemicals in a three-dimensional volume with a non-invasive and label-free manner. In view of the problems of existing data collection strategy, a frequency modulation and spatial encoding based Raman tomography was proposed, which aims to improve the data collection scheme and reduce the data collection time. In this scheme, the laser beam was divided into several sub-beams to use as multipoint excitation light sources. These sub-beams were first modulated with different frequencies and then incident on the different points of sample surface simultaneously. Because the excited Raman signals would carry such modulation information, the Raman signals from which excitation position can be distinguished with the demodulation process. In detection end, the Raman scattering light first passed through a spatial-encoding mask and then was directed to the single photomultiplier tube. By changing the pattern of the mask and then performing recovery with sparse reconstruction, the distribution of the Raman signals on the sample surface can be obtained based on compressive sensing theory. Preliminary results showed that our scheme can recover the Raman images to the certain extent with a better signal-to-noise ratio, demonstrating the proposed scheme is feasible.
Stimulated Raman scattering (SRS) microscopy has been increasingly used in biology and medicine in recent years due to its ability to image chemical bonds without labelling. Traditional SRS imaging uses Gaussian beams as the excitation sources, which can achieve high spatial and axial resolutions because of the tight focus of the Gaussian beam. However, the tight focus poses serious problems for observing the scattering media. The Gaussian beam would defocus after propagating through a small distance in scattering media. The SRS microscopy cannot work well in this case. Having the self-healing property, Bessel beams may bring solution to this problem. In our previous work, we applied the Bessel beams to the SRS and implemented three-dimensional SRS imaging with projection concept. Here, we simulated the propagation of Bessel beams and the generation of SRS signals with the beam propagation method (BPM). By adding glass beads on the beam propagation path to simulate scatters, the propagation of the Bessel beams and the generation of the SRS signals would change. We designed a series of simulations to investigate the influence of the size and position of the added glass beads to the generation of SRS signals. Simulation results demonstrated that the SRS signals can generate or be recovered at the certain depth in scattering media.
Cerenkov fluorescence imaging (CLI) has set a bridge between optical and nuclear imaging technologies by using an optical method to detect the distribution of radiotracers. Combining the emerged CLI technique with a clinical endoscope, the Cerenkov luminescence endoscope (CLE) was developed to avoid the problem of the poor penetration depth of the Cerenkov light. However, due to low energy of the Cerenkov light and the transportation loss during endoscopic imaging, the acquisition time of CLE signal is long and the imaging results are poor, which has limited the clinical applications of CLE. There are two ways to improve the availability of the current CLE system. First is to enhance the emitted signals of the Cerenkov light at the source end by developing new kinds of imaging probes or selecting high yield radionuclides. However, this will introduce the in vivo unfriendly problem in clinical translations. The second method is to improve the detection sensitivity of CLE system by optimizing the structure of the system. Here, we customized four endoscopes with different field of view (FOV) angles of endoscope probe and different monofilament diameters of imaging fiber bundles. By comparing the results obtained by different CLE systems, we optimized the parameters of system. The CLE imaging of 18F-FDG showed that when the distance between the probe and radionuclide source was fixed, smaller angle of FOV and lager monofilament diameter will provide higher collection efficiency.
As an emerging volumetric imaging technique, Stimulated Raman projection tomography (SRPT) can provide quantitative distribution of chemical components in a three-dimensional (3D) volume, with a label-free manner. Currently, the filtered back-projection (FBP) algorithm is used to reconstruct the 3D volume in SRPT. However, to obtain a satisfactory reconstruction result, the FBP algorithm requires a certain amount of projection data, usually, at least 180 projections in a half circle. This leads to a long data acquisition time and hence limits dynamic and longitudinal observation of living systems. Iterative reconstruction from sparsely sampled data may reduce the total data acquisition time by reducing the projections used in the reconstruction. In this work, two total variation regularization based iterative reconstruction algorithms were selected and used in SRPT, including the simultaneous algebra reconstruction technique (SART) and the two-step iterative shrinkage/thresholding algorithm (TwIST). The well-known distance-driven model was utilized as the forward and back-projectors. We evaluated these two algorithms with numerical simulations. Using the original image as the reference, we calculated the quality of the reconstructed images. Simulation results showed that both the SART and TwIST performed better than the FBP algorithm, with larger values of the structural similarity (SSIM). Furthermore, the number of projection images can be largely reduced when the iterative reconstruction algorithm was used. Especially when the SART was used, the projection number can be reduced to 15, providing a satisfactory reconstruction image (SSIM is larger than 0.9).