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Convolution-based scatter correction using kernels combining measurements and Monte Carlo simulations

N. Bhatia 1, * D. Tisseur 1 J.M. Létang 2
* Corresponding author
2 Imagerie Tomographique et Radiothérapie
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : One of the well-recognized challenge of Cone-Beam Computed Tomography (CBCT) is scatter contamination within the projection images. Scatter degrades the image quality by decreasing the contrast, introducing cupping and shading artifacts and thus leading to inaccuracies in the reconstructed values. The higher scatter to primary ratio experienced in industrial applications leads to even more important artifacts. Various strategies have been investigated to manage the scatter signal in CBCT projection data. One of these strategies is to calculate the scatter intensity by deconvolution of primary intensity using Scatter Kernel Superposition (SKS). In this paper, we present an approach combining experimental measurements and Monte Carlo simulations to estimate the scatter kernels for industrial applications based on the continuously thickness-adapted kernels strategy with a four-Gaussian modeling of kernels. We compare this approach with an experimental technique based on a two-Gaussian modeling of the kernels. The results obtained prove the superiority of a four-Gaussian model to effectively take into account both the contribution of object and detector scattering as compared to a two-Gaussian approach. We also present the parameterisation of the scatter kernels with respect to object to detector distance. This approach facilitates the use of a single geometry for calculation of scatter kernels over the whole magnification range of the acquisition setup.
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https://hal-cea.archives-ouvertes.fr/cea-01845382
Contributor : Léna Le Roy <>
Submitted on : Friday, July 20, 2018 - 1:14:41 PM
Last modification on : Wednesday, July 8, 2020 - 12:44:06 PM

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N. Bhatia, D. Tisseur, J.M. Létang. Convolution-based scatter correction using kernels combining measurements and Monte Carlo simulations. Journal of X-Ray Science and Technology, 2017, 25 (4), pp.613-628. ⟨10.3233/XST-16185⟩. ⟨cea-01845382⟩

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