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Communication Dans Un Congrès Année : 2018

A Constrained Gauss-Newton Algorithm for Material Decomposition in Spectral Computed Tomography

Résumé

Spectral computed tomography exploits energy-resolved detectors to recover the material composition of an object. Material decomposition is a challenging nonlinear and ill-posed inverse problem. While regularization improves the decomposition , the resulting material maps do not satisfy physical constraints (e.g., positivity). In this work, we propose a fast second-order algorithm for constrained material decomposition. The proposed constrained Gauss-Newton algorithm is compared to a standard (unconstrained) Gauss-Newton algorithm on two realistic numerical phantoms. An improved decomposition is obtained for both phantoms. We also found that the constraints must be enforced progressively during the iterations.
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Dates et versions

hal-01825613 , version 1 (28-06-2018)

Identifiants

Citer

Tom Hohweiller, Nicolas Ducros, Françoise Peyrin, Bruno Sixou. A Constrained Gauss-Newton Algorithm for Material Decomposition in Spectral Computed Tomography. ISBI 2018 IEEE International Symposium on Biomedical Imaging, IEEE, Apr 2018, Washington, United States. ⟨10.1109/ISBI.2018.8363587⟩. ⟨hal-01825613⟩
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