We have developed three types of prototype cameras based on multi-purpose and DSLR models equipped with full-frame (35 mm) CMOS sensors, i.e. modified versions of the Canon ME20F-SH, EOS-1D X, and EOS 5DS. We have also made a prototype interchangeable lens that is highly transmissive in the visible and near infrared (NIR) region, based on a Canon EF 100-400 mm lens. High signal-to-noise (S/N) ratio spectral images up to 1,100 nm were observed using the following tools: the modified ME20F-SH, which features a monochromatic sensor with a 19 μm pixel pitch; the modified EF 100-400 mm lens; NIR optical bandpass filters ranging from 1,000 to 1,200 nm with 25 nm bandwidth; and a short wavelength cut-off filter that blocks light below 840 nm. This camera and lens, with a liquid crystal tunable filter (LCTF) attached, was able to clearly observe absorption bands attributed to hydroxyl groups near 960 nm in aqueous sucrose solutions. This experimental set was also applied to discrimination between dairy cream and whipped topping in combination with multivariate analysis; multi spectral data set obtained by this experimental set was processed by principal component analysis followed by a modified algorithm of independent component analysis. The Canon EOS-1D X’s 18.1 million pixel sensor was modified by introducing NIR pixels in addition to its RGB pixels. The modified EOS-1D X and the lens were able to acquire both high-image-quality photography and high-resolution NDVI (Normalized Difference Vegetation Index) in the observation of a lawn with a single press of the shutter button. This camera and lens set was also applied to observe hair in dried seaweed, producing a high-resolution NIR image in which each hair was visualized. Furthermore, a sharper image of the hair was obtained using the modified EOS 5DS and the LCTF.
Stimulated Raman scattering (SRS) spectral microscopy is a promising imaging method, based on vibrational
spectroscopy, which can visualize biological tissues with chemical specificity. SRS spectral microscopy has been used to
obtain two-dimensional spectral images of rat liver tissue, three-dimensional images of a vessel in rat liver, and in vivo
spectral images of mouse ear skin. Various multivariate analysis techniques, such as principal component analysis and
independent component analysis, have been used to obtain spectral images. In this study, we propose a digital staining
method. This method uses SRS spectra and statistical machine learning that makes use of prior knowledge of spectral
peaks and their two-dimensional distributional patterns corresponding to the composition of tissue samples. The method
selects spectral peaks on the basis of Mahalanobis distance, which is defined as the ratio of inter-group variation to intragroup
variation. We also make use of higher-order local autocorrelations as feature values for two-dimensional
distributional patterns. This combination of techniques allows groups corresponding to different intracellular structures
to be clearly discriminated in the multidimensional feature space. We investigate the performance of our method on
mouse liver tissue samples and show that the proposed method can digitally stain each intracellular structure such as cell
nuclei, cytoplasm, and erythrocytes separately and clearly without time-consuming chemical staining processes. We
anticipate that our method could be applied to computer-aided pathological diagnosis.
We previously reported the combination of the high-speed stimulated Raman scattering (SRS) microscope and the multivariate analysis (principal component analysis and independent component analysis) for the tissue imaging. The results indicated the visualization of tissue components without chemical staining. Here, we report the multi-area observation of the tumor-grafted mouse tissue based on the same approach. The tumor-grafted mouse (balb/cAcJ nu/nu) was prepared by injection of human pancreatic carcinoma cell line (SUIT-2) into the tail of pancreas. Both of the pancreas cancer and the liver metastasis were harvested and fixed in formalin. Tissues were cryo-sectioned with a thickness of 100 μm and observed. The multispectral images (130 μm square, 500 x 500 pixels) of C-H vibration range from 2800 to 3100cm<sup>-1</sup> (91 different Raman shift images) were obtained at a frame rate of 30 frames/sec. The data acquisition both of pancreas and liver were continued for the 48 adjacent areas for the observation both of cancerous and non-cancerous region, respectively. All the datasets were combined to analyze for multivariate analysis. We propose a protocol for drastic data reduction, which we found to give reproducible results and allows fast calculation of ICA. The independent component images indicated the different shapes and compositions between the cancerous region and the non-cancerous region.
We have developed a video-rate stimulated Raman scattering (SRS) microscope with frame-by-frame wavenumber tunability. The system uses a 76-MHz picosecond Ti:sapphire laser and a subharmonically synchronized, 38-MHz Yb fiber laser. The Yb fiber laser pulses are spectrally sliced by a fast wavelength-tunable filter, which consists of a galvanometer scanner, a 4-f optical system and a reflective grating. The spectral resolution of the filter is ~ 3 cm<sup>-1</sup>. The wavenumber was scanned from 2800 to 3100 cm<sup>-1</sup> with an arbitrary waveform synchronized to the frame trigger. For imaging, we introduced a 8-kHz resonant scanner and a galvanometer scanner. We were able to acquire SRS images of 500 x 480 pixels at a frame rate of 30.8 frames/s. Then these images were processed by principal component analysis followed by a modified algorithm of independent component analysis. This algorithm allows blind separation of constituents with overlapping Raman bands from SRS spectral images. The independent component (IC) spectra give spectroscopic information, and IC images can be used to produce pseudo-color images. We demonstrate various label-free imaging modalities such as 2D spectral imaging of the rat liver, two-color 3D imaging of a vessel in the rat liver, and spectral imaging of several sections of intestinal villi in the mouse. Various structures in the tissues such as lipid droplets, cytoplasm, fibrous texture, nucleus, and water-rich region were successfully visualized.