TY  - JOUR
JF  - Inverse Problems
KW  - Science & Technology
KW  -  Physical Sciences
KW  -  Mathematics
KW  -  Applied
KW  -  Physics
KW  -  Mathematical
KW  -  Mathematics
KW  -  Physics
KW  -  x-ray tomography
KW  -  parallel beam tomography
KW  -  optimal projections
KW  -  Bayesian experimental design
KW  -  A-optimality
KW  -  D-optimality
KW  -  sequential optimization
KW  -  OPTIMAL EXPERIMENTAL-DESIGN
KW  -  BAYESIAN EXPERIMENTAL-DESIGN
KW  -  A-OPTIMAL DESIGN
KW  -  INVERSE PROBLEMS
A1  - Burger, M
A1  - Hauptmann, A
A1  - Helin, T
A1  - Hyvonen, N
A1  - Puska, JP
ID  - discovery10130687
N2  - 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.
PB  - IOP PUBLISHING LTD
UR  - http://dx.doi.org/10.1088/1361-6420/ac01a4
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
IS  - 7
TI  - Sequentially optimized projections in x-ray imaging *
EP  - 25
AV  - public
VL  - 37
Y1  - 2021/07/01/
ER  -