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)
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)
This paper presents a software-based technique able to incorporate a high level of prior knowledge related to a specific object in the Computed Tomography (CT) image reconstruction process. The scanning setup evaluated in this work, which comprises a static X-ray source and a detector, allows a higher object throughput compared to conventional CT systems.(More)
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