Total image constrained diffusion tensor for spectral computed tomography reconstruction

Photon counting detector (PCD)-based spectral computed tomography (CT) is a promising imaging technique that enables high energy resolution imaging with narrow energy bins. However, the image quality is degraded because the number of photons in each energy bin is less than the number of photons in the full spectrum. To reconstruct high quality spectral CT images with narrow energy bins, we developed a total image constrained diffusion tensor (TICDT) for statistical iterative reconstruction (SIR) based on a penalized weighted least-squares (PWLS) principle, which is called “PWLS-TICDT.” Specifically, TICDT uses supplementary information from a high-quality total image as a structural prior for SIR, so that the narrow energy bin image can be enhanced, while some primary features are preserved. We also developed an alternating minimization algorithm to solve the associated objective function. We conducted qualitative and quantitative studies to validate and evaluate the PWLS-TICDT method using digital phantoms and preclinical data. Results from both numerical simulation and real PCD data studies show that the proposed PWLS-TICDT method achieves noticeable gains over competing methods in terms of suppressing noise, detecting low contrast objects, and preserving resolution. More importantly, the multi-energy images reconstructed by PWLS-TICDT method can generate more accurate basis material decomposition results than the other methods.