Blind beam-hardening correction from Poisson measurements
Date
2016-02-10
Authors
Gu, Renliang
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AIP Publishing LLC
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Electrical and Computer Engineering
Abstract
We develop a sparse image reconstruction method for Poisson-distributed polychromatic X-ray computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. We employ our mass-attenuation spectrum parameterization of the noiseless measurements and express the mass- attenuation spectrum as a linear combination of B-spline basis functions of order one. A block coordinate-descent algorithm is developed for constrained minimization of a penalized Poisson negative log-likelihood (NLL) cost function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and nonnegativity and sparsity of the density map image; the image sparsity is imposed using a convex total-variation (TV) norm penalty term. This algorithm alternates between a Nesterov’s proximal-gradient (NPG) step for estimating the density map image and a limited-memory Broyden-Fletcher-Goldfarb-Shanno with box constraints (L-BFGS-B) step for estimating the incident-spectrum parameters. To accelerate convergence of the density- map NPG steps, we apply function restart and a step-size selection scheme that accounts for varying local Lipschitz constants of the Poisson NLL. Real X-ray CT reconstruction examples demonstrate the performance of the proposed scheme.
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This proceeding appeared in Gu, Renliang, and Aleksandar Dogandžić. "Blind beam-hardening correction from Poisson measurements." In AIP Conference Proceedings, vol. 1706, no. 1, p. 110010. AIP Publishing LLC, 2016, and may be found at
DOI: 10.1063/1.4940581.
Copyright 2016 AIP Publishing LLC.
Posted with permission.