Iterative image reconstruction for dual-energy X-ray CT using regularized material sinogram estimates

Abstract

X-ray CT images have various applications, including CT-based attenuation correction (CTAC) for PET. Low-dose CT imaging is particularly desirable for CTAC. Dual-energy (DE) CT imaging methods may improve the accuracy of attenuation correction in PET. However, conventional DE CT approaches to sinogram material decomposition use logarithmic transforms that are sensitive to noise in low-dose scans. This paper describes a DE reconstruction method based on statistical models that avoids using a logarithm. We first estimate material sinograms directly from the raw DE data (without any logarithm), with mild regularization to control noise and avoid outliers. We then apply a penalized weighted least squares (PWLS) method to reconstruct images of the two material components. We also propose a joint edge-preserving regularizer that uses the prior knowledge that the two material images have many region edges located in the same positions. Preliminary simulation results suggest that this iterative method improves image quality compared to conventional approaches based on log data for low-dose DE CT scans.