A High-Quality Photon-Counting CT Technique Based on Weight Adaptive Total-Variation and Image-Spectral Tensor Factorization for Small Animals Imaging
Weiwen Wu, Dianlin Hu, Kang An, Shaoyu Wang, Fulin Luo
Abstract
Photon-counting X-ray computed tomography (CT) has been attracting great attention in tissue characterization, material discrimination, and so on. The emitting X-ray energy spectrum cutting into several energy bins that can result in only a part of X-ray photons can be collected within each narrow bin. This can compromise the image quality. In this case, how to obtain high-quality tomography is a big challenge. In this study, to overcome these issues, we mainly focus on developing an advanced imaging software based on the latest photon-counting CT system (MARS scanner). Specifically, we first design a weight adaptive total variation (TV) using compressed sensing theory. Then, combining the weight adaptive TV and nonlocal low-rank tensor factorization to formulate a new weight adaptive total-variation and image-spectral tensor factorization (WATITF) model for high-quality imaging. Finally, the optimization model is performed to obtain its solution. The studies including the numerical and preclinical mice are performed to validate and evaluate its outperformance.