Implementation of penalized-likelihood statistical reconstruction for polychromatic dual-energy CT

This paper presents a statistical reconstruction algorithm for dual-energy (DE) CT of polychromatic x-ray source. Each pixel in the imaged object is assumed to be composed of two basis materials (i.e., bone and soft tissue) and a penalizedlikelihood objective function is developed to determine the densities of the two basis materials. Two penalty terms are used respectively to penalize the bone density difference and the soft tissue density difference in neighboring pixels. A gradient ascent algorithm for monochromatic objective function is modified to maximize the polychromatic penalizedlikelihood objective function using the convexity technique. In order to reduce computation consumption, the denominator of the update step is pre-calculated with reasonable approximation replacements. Ordered-subsets method is applied to speed up the iteration. Computer simulation is implemented to evaluate the penalized-likelihood algorithm. The results indicate that this statistical method yields the best quality image among the tested methods and has a good noise property even in a lower photon count.