Sequentially optimized projections in x-ray imaging *

Burger M, Hauptmann A, Helin T, Hyvonen N, Puska JP (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Inverse Problems Institute of Physics: Hybrid Open Access

Book Volume: 37

Journal Issue: 7

DOI: 10.1088/1361-6420/ac01a4

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.

Authors with CRIS profile

Martin Burger Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Involved external institutions

Oulun Yliopisto / University of Oulo FI Finland (FI) Lappeenranta University of Technology (LUT) / Lappeenrannan teknillinen yliopisto FI Finland (FI) Aalto University Espoo FI Finland (FI)

How to cite

APA:

Burger, M., Hauptmann, A., Helin, T., Hyvonen, N., & Puska, J.P. (2021). Sequentially optimized projections in x-ray imaging *. Inverse Problems, 37(7). https://dx.doi.org/10.1088/1361-6420/ac01a4

MLA:

Burger, Martin, et al. "Sequentially optimized projections in x-ray imaging *." Inverse Problems 37.7 (2021).

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