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Sequentially optimized projections in X-ray imaging

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Document pages: 23 pages

Abstract: This work applies Bayesian experimental design to selecting optimalprojection geometries in (discretized) parallel beam X-ray tomography assumingthe prior and the additive noise are Gaussian. The introduced greedy exhaustiveoptimization algorithm proceeds sequentially, with the posterior distributioncorresponding to the previous projections serving as the prior for determiningthe design parameters, i.e. the imaging angle and the lateral position of thesource-receiver pair, for the next one. The algorithm allows redefining theregion of interest after each projection as well as adapting parameters in the(original) prior to the measured data. Both A and D-optimality are considered,with emphasis on efficient evaluation of the corresponding objective functions.Two-dimensional numerical experiments demonstrate the functionality of theapproach.

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