Computer vision for biomedical imaging applications is fast developing and at once demanding field of computerscience. In particular, computer vision technique provides excellent results for detection and segmentation problems intomographic imaging. X-ray phase contrast Tomography (XPCT) is a noninvasive 3D imaging technique with highsensitivity for soft tissues. Despite a considerable progress in XPCT data acquisition and data processing methods, theproblem in degradation of image quality due to artifacts remains a widespread and often critical issue for computervision applications. One of the main problems originates from a sample alteration during a long tomographic scan. Weproposed and tested Simultaneous Iterative Reconstruction algorithm with Total Variation regularization to reduce thenumber of projections in high resolution XPCT scans of ex-vivo mouse spinal cord. We have shown that the proposedalgorithm allows tenfold reducing the number of projections and, therefore, the exposure time, with conservation of theimportant morphological information in 3D image with quality acceptable for computer graphics and computer visionapplications. Our research paves a way for more effective implementation of advanced computer technologies in phasecontrast tomographic research.
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