@article{discovery10130687,
           title = {Sequentially optimized projections in x-ray imaging *},
            year = {2021},
       publisher = {IOP PUBLISHING LTD},
         journal = {Inverse Problems},
           month = {July},
          number = {7},
          volume = {37},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          author = {Burger, M and Hauptmann, A and Helin, T and Hyvonen, N and Puska, JP},
        abstract = {This work applies Bayesian experimental design to selecting optimal projection
geometries in (discretized) parallel beam X-ray tomography assuming the prior and the additive
noise are Gaussian. The introduced greedy exhaustive optimization algorithm proceeds sequentially,
with the posterior distribution corresponding to the previous projections serving as the prior for
determining the design parameters, i.e. the imaging angle and the lateral position of the sourcereceiver pair, for the next one. The algorithm allows redefining the region 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 the approach.},
             url = {http://dx.doi.org/10.1088/1361-6420/ac01a4},
        keywords = {Science \& Technology, Physical Sciences, Mathematics, Applied, Physics, Mathematical, Mathematics, Physics, x-ray tomography, parallel beam tomography, optimal projections, Bayesian experimental design, A-optimality, D-optimality, sequential optimization, OPTIMAL EXPERIMENTAL-DESIGN, BAYESIAN EXPERIMENTAL-DESIGN, A-OPTIMAL DESIGN, INVERSE PROBLEMS}
}