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Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography

Kazantsev, D; Jørgensen, JS; Andersen, MS; Lionheart, WRB; Lee, PD; Withers, PJ; (2018) Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography. Inverse Problems , 34 (6) , Article 064001. 10.1088/1361-6420/aaba86. Green open access

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Abstract

Rapid developments in photon-counting and energy-discriminating detectors have the potential to provide an additional spectral dimension to conventional x-ray grayscale imaging. Reconstructed spectroscopic tomographic data can be used to distinguish individual materials by characteristic absorption peaks. The acquired energy-binned data, however, suffer from low signal-to-noise ratio, acquisition artifacts, and frequently angular undersampled conditions. New regularized iterative reconstruction methods have the potential to produce higher quality images and since energy channels are mutually correlated it can be advantageous to exploit this additional knowledge. In this paper, we propose a novel method which jointly reconstructs all energy channels while imposing a strong structural correlation. The core of the proposed algorithm is to employ a variational framework of parallel level sets to encourage joint smoothing directions. In particular, the method selects reference channels from which to propagate structure in an adaptive and stochastic way while preferring channels with a high data signal-to-noise ratio. The method is compared with current state-of-the-art multi-channel reconstruction techniques including channel-wise total variation and correlative total nuclear variation regularization. Realistic simulation experiments demonstrate the performance improvements achievable by using correlative regularization methods.

Type: Article
Title: Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1361-6420/aaba86
Publisher version: https://doi.org/10.1088/1361-6420/aaba86
Additional information: Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Keywords: multi-spectral, image reconstruction, structural regularization, inverse problems, total variation, materials science, x-ray imaging
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10050350
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