Alzheimer's disease (AD) remains one of the foremost public health challenges of our time. Recently, attention has turned to the gut-brain axis, a complex network of communication between the gastrointestinal tract and the brain, as a potential player in the pathogenesis of AD. Here we exploited x-ray Phase Contrast Tomography to provide an in-depth analysis of the link between the gut condition and AD, exploring gut anatomy and structure in murine models. We conducted a comprehensive analysis by comparing the outcomes in various mouse models of cognitive impairment, including AD, frail mice, and frontotemporal dementia affected mice. We discovered an association between substantial changes in the gut structure and the presence of amyloid-beta (Aβ) in the brain. We found that the most important gut alterations are related to Aβ occurrence in the brain. In particular, we investigated the gut morphology, the distribution of enteric micro-processes and neurons in the ileum.
X-ray phase-contrast tomography (XPCT) offers a highly sensitive 3D imaging approach to investigate different disease-relevant networks from the single cell to the whole organ. We present here a concomitant study of the evolution of tissue damage and inflammation in potential target organs of the disease in the murine model of multiple sclerosis. XPCT identifies and monitors structural and cellular alterations throughout the central nervous system, but also in the gut and eye, of mice induced to develop multiple sclerosis-like disease and sacrificed at pre-symptomatic and symptomatic time points. This approach rests on a multiscale analysis to detect early appearance of imaging indicators potentially acting as biomarkers predictive of the disease. The longitudinal data permit an original evaluation of the sequential evolution of multi-organ damage in the mouse model, shedding light on the role of the gut-brain axis in the disease initiation and progression, of relevance for the human case.
Computer vision for biomedical imaging applications is fast developing and at once demanding field of computer science. In particular, computer vision technique provides excellent results for detection and segmentation problems in tomographic imaging. X-ray phase contrast Tomography (XPCT) is a noninvasive 3D imaging technique with high sensitivity for soft tissues. Despite a considerable progress in XPCT data acquisition and data processing methods, the problem in degradation of image quality due to artifacts remains a widespread and often critical issue for computer vision applications. One of the main problems originates from a sample alteration during a long tomographic scan. We proposed and tested Simultaneous Iterative Reconstruction algorithm with Total Variation regularization to reduce the number of projections in high resolution XPCT scans of ex-vivo mouse spinal cord. We have shown that the proposed algorithm allows tenfold reducing the number of projections and, therefore, the exposure time, with conservation of the important morphological information in 3D image with quality acceptable for computer graphics and computer vision applications. Our research paves a way for more effective implementation of advanced computer technologies in phase contrast tomographic research.
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