Statistical reconstruction of material decomposed data in spectral CT.

Photon-counting detector technology has enabled the first experimental investigations of energy-resolved computed tomography (CT) imaging and the potential use for K-edge imaging. However, limitations in regards to detecter technology have been imposing a limit to effective count rates. As a consequence, this has resulted in high noise levels in the obtained images given scan time limitations in CT imaging applications. It has been well recognized in the area of low-dose imaging with conventional CT that iterative image reconstruction provides a superior signal to noise ratio compared to traditional filtered backprojection techniques. Furthermore, iterative reconstruction methods also allow for incorporation of a roughness penalty function in order to make a trade-off between noise and spatial resolution in the reconstructed images. In this work, we investigate statistically-principled iterative image reconstruction from material-decomposed sinograms in spectral CT. The proposed reconstruction algorithm seeks to minimize a penalized likelihood-based cost functional, where the parameters of the likelihood function are estimated by computing the Fisher information matrix associated with the material decomposition step. The performance of the proposed reconstruction method is quantitatively investigated by use of computer-simulated and experimental phantom data. The potential for improved K-edge imaging is also demonstrated in an animal experiment.