KEYWORDS: Colon, Magnetic resonance imaging, Confocal microscopy, In vivo imaging, Endomicroscopy, Decision support systems, Microscopy, Luminescence, Visualization, Video
The objective of our study is to develop a multimodality approach by combining magnetic resonance imaging (MRI) and optical imaging methods to assess acute murine colitis at the macro- and microscopic level. In vivo MRI is used to measure the cross-sectional areas of colons at the macroscopic level. Dual-color confocal laser endomicroscopy (CLE) allows in vivo examination of the fluorescently labeled epithelial cells and microvessels in the mucosa with a spatial resolution of ∼1.4 μm during ongoing endoscopy. To further validate the structural changes of the colons in three-dimensions, ex vivo light-sheet fluorescence microscopy (LSFM) is applied for in-toto imaging of cleared colon sections. MRI, LSFM, and CLE findings are significantly correlated with histological scoring (p < 0.01) and the inflammation-associated activity index (p < 0.01). Our multimodality imaging technique permits visualization of mucosa in colitis at different scales, which can enhance our understanding of the pathogenesis of inflammatory bowel diseases.
KEYWORDS: Colon, Magnetic resonance imaging, Microscopy, Two photon excitation microscopy, Inflammation, Optical imaging, In vivo imaging, Biomedical optics
Non-invasive imaging technologies, such as magnetic resonance imaging (MRI) and optical multimodality imaging methods, are commonly used for diagnosing and supervising the development of inflammatory bowel disease (IBD). These in vivo imaging methods can provide morphology changes information of IBD in macro-scale. However, it is difficult to investigate the intestinal wall in molecular and cellular level. State-of-art light-sheet and two-photon microscopy have the ability to acquire the changes for IBD in micro-scale. The aim of this work is to evaluate the size of the enterocoel and the thickness of colon wall using both MRI for in vivo imaging, and light-sheet and two-photon microscope for in vitro imaging. C57BL/6 mice were received 3.5% Dextran sodium sulfate (DSS) in the drinking water for 5 days to build IBD model. Mice were imaged with MRI on days 0, 6 to observe colitis progression. After MRI imaging, the mice were sacrificed to take colons for tissue clearing. Then, light-sheet and two-photon microscopies are used for in vitro imaging of the cleared samples. The experimental group showed symptoms of bloody stools, sluggishness and weight loss. It showed that the colon wall was thicker while the enterocoel was narrower compare to control group. The more details are observed using light-sheet and two-photon microscope. It is demonstrated that hybrid of MRI in macro-scale and light-sheet and two-photon microscopy in micro-scale imaging is feasible for colon inflammation diagnosing and supervising.
Among the detected small nodules sized from 3 to 30mm in CT images, a significant portion is undetermined in terms of malignancy which needs biopsy or other follow-up means, resulting in excessive risk and cost. Therefore, predicting the malignancy of the nodules becomes a clinically desirable task. Based on the previous study of texture features extracted from gray-tone spatial-dependence matrices, this study aims to find more efficient texture features or image texture markers in discriminating the nodule malignancy. Two new image texture markers (median and variance) are proposed to classify the small nodules into different malignant levels, thus the risk prediction could be performed through image analysis. These two new image texture markers can minimize the effect of outliers in the feature series, thus can reduce the noise influence to the feature classification. Total 1,353 nodule samples selected from the Lung Image Database Consortium were used to evaluate the efficiency of the proposed new features. All the classification results are shown in the ROC curves and tabulated by the AUC values. The classification outcomes from (1) the most likely and likely benign nodules vs. the most likely and likely malignant nodules, (2) the most likely vs. likely benign nodules, and (3) the most likely vs. likely malignant nodules, are 0.9125±0.0096, 0.9239±0.0147, and 0.8888±0.0197, respectively, in terms of the largest AUC values. From the experimental outcomes on different malignant levels, the two new image texture markers from nodule volumetric CT image data have shown encouraging performance for the risk prediction.
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