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A hybrid approach to imaging and anomaly characterization from dual energy CT data

In this paper we present a novel polychromatic dual energy algorithm with an emphasis on detection of anomalies whose physical properties are assumed to be known with some level of uncertainty. We assume that material characteristics are defined by energy independent Compton scatter and photoelectric absorption coefficients. Uncertainty in material properties are characterized by an elliptical constraint regions in the Compton scatterphotoelectric coefficient space. We employ an image based iterative reconstruction algorithm to produce images of Compton scatter and photoelectric absorption coefficients of the medium. The solution is obtained via a nonlinear optimization process where the prior knowledge about the characteristics of object of interest is imposed as hard constraints. We also introduce a novel gradient-based similarity regularizer to cope with physics based limitations on accurately reconstructing the photoelectric absorption coefficient component. Our approach is based on a parametric level-set representation of the characteristic function of the object. For the reconstruction of the background we use basis expansion approach using compactly supported exponential radial basis functions. Numerical results show that the algorithm gives results superior to conventional filtered back projection (FBP) dual energy method in the presence of noise.