Version 1
: Received: 15 April 2024 / Approved: 15 April 2024 / Online: 15 April 2024 (13:47:51 CEST)
How to cite:
Fathi, H.; Van Leeuwen, T. Source Design Optimization for Depth Image Reconstruction in X-ray Imaging. Preprints2024, 2024040960. https://doi.org/10.20944/preprints202404.0960.v1
Fathi, H.; Van Leeuwen, T. Source Design Optimization for Depth Image Reconstruction in X-ray Imaging. Preprints 2024, 2024040960. https://doi.org/10.20944/preprints202404.0960.v1
Fathi, H.; Van Leeuwen, T. Source Design Optimization for Depth Image Reconstruction in X-ray Imaging. Preprints2024, 2024040960. https://doi.org/10.20944/preprints202404.0960.v1
APA Style
Fathi, H., & Van Leeuwen, T. (2024). Source Design Optimization for Depth Image Reconstruction in X-ray Imaging. Preprints. https://doi.org/10.20944/preprints202404.0960.v1
Chicago/Turabian Style
Fathi, H. and Tristan Van Leeuwen. 2024 "Source Design Optimization for Depth Image Reconstruction in X-ray Imaging" Preprints. https://doi.org/10.20944/preprints202404.0960.v1
Abstract
X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited angle tomography can be used to reduce the amount of data that needs to be acquired and thus speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths.
In this paper, we investigate an optimal experimental design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in case where moving the source and detector is time consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
