Endometrial cancer; cylindrical diffuser; photoacoustic imaging

This study aimed to explore the application of surface-enhanced Raman scattering (SERS) in the rapid diagnosis of gastric cancer. The SERS spectra of 68 serum samples from gastric cancer patients and healthy volunteers were acquired. The characteristic ratio method (CRM) and principal component analysis (PCA) were used to differentiate gastric cancer serum from normal serum. Compared with healthy volunteers, the serum SERS intensity of gastric cancer patients was relatively high at 722cm-1, while it was relatively low at 588, 644, 861, 1008, 1235, 1397, 1445 and 1586cm-1. These results indicated that the relative content of nucleic acids in the serum of gastric cancer patients rises while the relative content of amino acids and carbohydrates decreases. In PCA, the sensitivity and specificity of discriminating gastric cancer were 94.1% and 94.1%, respectively, with the accuracy of 94.1%. Based on the intensity ratios of four characteristic peaks at 722, 861, 1008 and 1397cm-1, CRM presented the diagnostic sensitivity and specificity of 100% and 97.4%, respectively, and the accuracy of 98.5%. Therefore, the three peak intensity ratios of I722/I861, I722/I1008 and I722/I1397 can be considered as biological fingerprint information for gastric cancer diagnosis and can rapidly and directly reflect the physiological and pathological changes associated with gastric cancer development. This study provides an important basis and standards for the early diagnosis of gastric cancer.

3D live imaging is important for better understanding of biological processes. To obtain biological dynamic process, high imaging speed is required. In order to improve speed in 3D live imaging, simultaneous imaging of multiple planes throughout a 3D volume has been proposed. However, a main disadvantage of this method is the cross-talk from neighboring imaging planes. In this paper, we propose an optimization method to suppress background from neighboring imaging planes. A D-aperture is used to generate multiple light sheets. An optimization method to suppress background is presented. The simulation results demonstrated that the proposed method can be used to suppress the effectiveness of background from neighboring light sheets.

As important components of air pollutant, volatile organic compounds (VOCs) can cause great harm to environment and human body. The concentration change of VOCs should be focused on in real-time environment monitoring system. In order to solve the problem of wavelength redundancy in full spectrum partial least squares (PLS) modeling for VOCs concentration analysis, a new method based on improved interval PLS (iPLS) integrated with Monte-Carlo sampling, called iPLS-MC method, was proposed to select optimal characteristic wavelengths of VOCs spectra. This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling. The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum. Different wavelength selection methods were built, respectively, on Fourier transform infrared (FTIR) spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory. When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times, the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10, which occupies only 0.22% of the full spectrum wavelengths. While the RMSECV and correlation coefficient (Rc) for ethylene are 0.2977 and 0.9999ppm, and those for ethanol gas are 0.2977 ppm and 0.9999. The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively, and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

Laser speckle contrast imaging (LSCI) is an optical imaging method, which can monitor microvascular flow variation directly without addition of any ectogenous dye. All the existing laser speckle contrast analysis (LASCA) methods are a combination of spatial and temporal statistics. In this study, we have proposed a new method, Gaussian kernel laser speckle contrast analysis (gLASCA), which processes the raw images primarily with the Gaussian kernel operator along the spatial direction of blood flow. We explored the properties of gLASCA in the simulation and animal cerebral ischemia perfusion model. Compared with the other existing speckle processing methods based on spatial, temporal, spatial-temporal or anisotropic linear structure; the present gLASCA method has a high spatial-temporal resolution to respond the change of velocity especially in microvasculature. Besides, the gLASCA method obtains approximately 10.2% and 7.1% higher contrast-to-noise ratio (CNR) over the anisotropic linear method (aLASCA) in the simulation and experiment models. For these advantages, gLASCA could be a better method for local microvascular laser speckle imaging in terms of cerebral ischemia reperfusion, spreading depression and brain injury diseases.

The detection of early gastric cancer that often develops asymptomatically is crucial for improving patient survival. The photodynamic diagnosis (PDD) of gastric cancer using 5-aminolevulinic acid/protoporphyrin IX (5-ALA/PpIX) has been reported in several studies. However, the selectivity of PDD of gastric tumor is poor with often false-positive results that require the development of new methods to improve PDD for early gastric cancer. Therefore, a measure of the complexity of gastric microcirculation (multi-scale entropy, MSE) and the detrended fluctuation analysis (DFA) were applied as additional tools to detect early gastric cancer in rats.In this experimental study, we used our original model of metastatic adenocarcinoma in the stomach of a rat. To induce a gastric tumor, we used a long-term combination (for 9 months, which is 1/2 of the life of rats) of two natural factors, such as chronic stress (overpopulation being typical for modern cities) and the daily presence of nitrites in food and drinks, which are common ingredients added to processed meat and fish to help preserve food. Our results clearly show that both methods, namely, PDD using 5-ALA/PpIX and complexity/correlation analysis, can detect early gastric cancer, which was confirmed by histological analysis. Pre-cancerous areas in the stomach were detected as an intermediate fluorescent signal or MSE level between normal and malignant lesions of the stomach. However, in some cases, PDD with 5-ALA/PpIX produced a false-positive fluorescence of exogenous fluorophores due to its accumulation in benign and inflammatory areas of the mucosa. This fact indicates that the PDD itself is not sufficient for the correct diagnosis of gastric cancer, and the use of additional characteristics, e.g., complexity measures or scaling exponents, can significantly improve the diagnostic accuracy of PDD of gastric cancer that should be confirmed in further clinical studies and applications.

Diffuse optical spectroscopy is a relatively new, noninvasive and nonionizing technique for breast cancer diagnosis. In the present study, we have introduced a novel handheld diffuse optical breast scan (DOB-Scan) probe to measure optical properties of the breast in vivo and create functional and compositional images of the tissue. In addition, the probe gives more information about breast tissue’s constituents, which helps distinguish a healthy and cancerous tissue. Two symmetrical light sources, each including four different wavelengths, are used to illuminate the breast tissue. A high-resolution linear array detector measures the intensity of the back-scattered photons at different radial destinations from the illumination sources on the surface of the breast tissue, and a unique image reconstruction algorithm is used to create four cross-sectional images for four different wavelengths. Different from fiber optic-based illumination techniques, the proposed method in this paper integrates multi-wavelength light-emitting diodes to act as pencil beam sources into a scattering medium like breast tissue. This unique design and its compact structure reduce the complexity, size and cost of a potential probe. Although the introduced technique miniaturizes the probe, this study points to the reliability of this technique in the phantom study and clinical breast imaging. We have received ethical approval to test the DOB-Scan probe on patients and we are currently testing the DOB-Scan probe on subjects who are diagnosed with breast cancer.

In most coherent imaging modality, speckle noise is a major cause that blurs the boundary of tissues and degrades the image contrast. By utilizing the unique properties of supercontinuum (SC) generated by noise-like pulses (NLPs) and a simple multi-frame averaging technique, we achieved significant speckle reduction in spectral domain optical coherence tomography (SD-OCT). We quantitatively compared the speckle of our proposed method with those of conventional swept source OCT (SS-OCT) and SD-OCT based on commercial light sources. The experimental results show that SC pumped by NLPs combined with noncoherent averaging method achieves better denoising performance in terms of contrast to noise ratio (CNR).

The purpose of this study is to examine optical spatial frequency spectroscopy analysis (SFSA) combined with visible resonance Raman (VRR) spectroscopic method, for the first time, to discriminate human brain metastases of lung cancers adenocarcinoma (ADC) and squamous cell carcinoma (SCC) from normal tissues. A total of 31 label-free micrographic images of three types of brain tissues were obtained using a confocal micro-Raman spectroscopic system. VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532nm from the same sites of the tissues. Using SFSA method, the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian function fitting. The standard deviations, σ calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine (SVM) classifier. The key VRR biomolecular fingerprints of carotenoids, tryptophan, amide II, lipids and proteins (methylene/methyl groups) were also analyzed using SVM classifier. All three types of brain tissues were identified with high accuracy in the two approaches with high correlation. The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues, which are on-site, real-time and label-free and may improve the accuracy of brain biopsy.