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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 source-receiver 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.
Original language | English |
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Article number | 075006 |
Number of pages | 25 |
Journal | Inverse Problems |
Volume | 37 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 2021 |
MoE publication type | A1 Journal article-refereed |
Keywords
- A-optimality
- Bayesian experimental design
- D-optimality
- optimal projec-tions
- parallel beam tomography
- sequential optimization
- x-ray tomography
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Dive into the research topics of 'Sequentially optimized projections in x-ray imaging'. Together they form a unique fingerprint.Projects
- 1 Finished
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Centre of Excellence of Inverse Modelling and Imaging
Hyvönen, N., Ojalammi, A., Puska, J., Kuutela, T., Perkkiö, L. & Hirvi, P.
01/01/2018 → 31/12/2020
Project: Academy of Finland: Other research funding