Iterative reconstruction for low dose dual energy CT using information-divergence constrained spectral redundancy information

Dual energy computed tomography (DECT) can improve the capability of differentiating different materials compared with conventional CT. However, due to non-negligible radiation exposure to patients, dose reduction has recently become a critical concern in CT imaging field. In this work, to reduce noise at the same time maintain DECT images quality, we present an iterative reconstruction algorithm for low-dose DECT images where in the objective function of the algorithm consists of a data-fidelity term and a regularization term. The former term is based on alpha-divergence to describe the statistical distribution of the DE sinogram data. And the latter term is based on the redundant information to reflect the prior information of the desired DECT images. For simplicity, the presented algorithm is termed as "AlphaD-aviNLM". To minimize the associative objective function, a modified proximal forward-backward splitting algorithm is proposed. Digital phantom, physical phantom, and patient data were utilized to validate and evaluate the presented AlphaD-aviNLM algorithm. The experimental results characterize the performance of the presented AlphaD-aviNLM algorithm. Speficically, in the digital phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 49%, 34%, and 40% gains for the RMSE metric, 1.3%, 0.4%, and 0.7% gains for the FSIM metric and 13%, 8%, and 11% gains for the PSNR metric. In the physical phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 0.55%, 0.07%, and 0.16% gains for the FSIM metric.