Andrei Dabravolski

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Object reconstruction from a series of projection images, such as in computed tomography (CT), is a popular tool in many different application fields. Existing commercial software typically provides sufficiently accurate and convenient-to-use reconstruction tools to the end-user. However, in applications where a non-standard acquisition protocol is used, or(More)
In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to(More)
In X-ray tomography, a number of radiographs (projections) are recorded from which a tomogram is then reconstructed. Conventionally, these projections are acquired equiangularly, which intrinsically assumes that the information added by each projection does not depend on the angular spacing. However, especially in case when only a limited number of(More)
BACKGROUND In computed tomography (CT), the source-detector system commonly rotates around the object in a circular trajectory. Such a trajectory does not allow to exploit a detector fully when scanning elongated objects. OBJECTIVE Increase the spatial resolution of the reconstructed image by optimal zooming during scanning. METHODS A new approach is(More)
slice of cheese SIRT S-SIRT DART DART using EOD conventional cone beam X-ray geometry conveyor belt X-ray CT: inline scanning geometry Fig. 1. CT reconstructions of a slice of cheese using conventional and inline scanning geometries. Due to the angular imaging constraints, the low number of available projections and the existence of truncated data, the(More)
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