Bayesian data fusion and inversion in X-ray multi-energy computed tomography

In this paper, we first introduce a Multi-Energy Computed Tomography (MECT) forward projection model based on the base material decomposition method. In this method, an object is considered into a linear combination of the fractions of several base materials weighted by X-ray beam energy functions. Then, three different data fusion inversion approaches are proposed to reconstruct the base material fractions. For the first pre-separation reconstruction approach, the base material decomposition is carried on in the projection space while in the second post-separation approach, the base material decomposition is carried on the attenuation coefficients. The third approach is a joint Bayesian inversion method. Finally, the reconstruction performances of the three reconstruction methods are compared on the simulated data.