United iterative reconstruction for spectral computed tomography

Spectral computed tomography (CT) has attracted considerable attention because of its energy-resolving capability in identifying and discriminating materials. The use of a narrow energy bin can improve energy resolution. However, a narrow energy bin has high noise ratio, which degrades the imaging quality of spectral CT. To address this problem, this study exploits the structure correlations of images in the energy domain and proposed two types of united iterative reconstruction (UIR) algorithms. One type uses the well-reconstructed broad-spectrum image, with all available photons, as a constraint, whereas the other type uses a pseudo narrow-energy image, which is estimated with the use of our proposed structure-coupling (SC) method, as a constraint. The SC method utilizes local structures to connect images that are reconstructed with broad-spectrum and narrow-energy CT datasets. Given a broad-spectrum image, the SC method can accurately estimate its corresponding narrow-energy image. Results show that UIR algorithms significantly outperform conventional iterative reconstruction algorithms for narrow-energy image reconstruction in spectral CT. Among the UIR algorithms, SC-UIR yields the best results.