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Pré-Publication, Document De Travail Année : 2016

Regularization of Nonlinear Decomposition of Spectral X-ray Projection Images

Résumé

Purpose: Exploiting the X-ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3D description of a patient in a material 10 basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x-ray projection images, which is a non-linear ill-posed problem. Methods: Our main contribution is to introduce a material-dependent spatial regularization scheme in the projection domain. The problem is efficiently solved iteratively using the Gauss-15 Newton algorithm and a linear solver that exploits the sparse nature of operators. The framework is validated on numerical experiments of a thorax phantom made of soft tissue, bone and gadolinium scanned with a 120 kV source and a 4-bin photon counting detector. Different scenarios are created to evaluate the method performance by varying the number of incident photons and the concentration of the marker. Convergence of the proposed method and its dependence on the 20 regularization parameter are also studied. Results : We show that material decomposition is feasible with the proposed method and that it converges in few iterations. For the assessed experiments, material decomposition was possible when the number of incident photons was equal or larger than 10 5 or when the marker concentration was equal or larger than 0.01 g.cm −3 for 10 6 photons. 25 Conclusions : The proposed method efficiently solves the nonlinear decomposition problem for spectral CT, which opens up new possibilities such as material-specific regularization in the projection domain and a parallelization framework, in which projections are solved in parallel.
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Dates et versions

hal-01391538 , version 1 (03-11-2016)
hal-01391538 , version 2 (07-04-2017)
hal-01391538 , version 3 (18-05-2017)

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  • HAL Id : hal-01391538 , version 1

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Nicolas Ducros, J F P J Abascal, B Sixou, S Rit, F Peyrin. Regularization of Nonlinear Decomposition of Spectral X-ray Projection Images. 2016. ⟨hal-01391538v1⟩
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