Statistical iterative material image reconstruction with patch based enhanced 3DTV regularization for photon counting CT

Photon counting computed tomography (PCCT) can simultaneously acquire measurements from multiple energies, and is able to differentiate material. However, material decomposition strategy typically leads to signal-tonoise ratio degradation and noise amplification due to limited photons detected at one energy bin in PCCT imaging. In this work, to address this issue, we present a statistical iterative material image reconstruction method to estimate material accurately. Specifically, the patch-based enhanced 3D total variation (PE3DTV) regularization is introduced into the statistical iterative model. Moreover, the PE3DTV extracts non-local similarities among all the desired material images, then stacks those similar patches to construct 3D tensor, and calculates the sparsity on the subspace of the 3D tensor based on gradient maps, encoding the correlation across nonlocal structures among material images. The numerical experiments show that the present method leads to reduced statistical bias and improved material image quality compared to the conventional TV-based method.