Compared to traditional computed tomography (CT), photon-counting detector (PCD)-based CT provides significant advantages, including enhanced CT image contrast and reduced radiation dose. However, owing to the current immaturity of PCD technology, scanned PCD data often contain stripe artifacts resulting from non-functional or defective detector units, which subsequently introduce ring artifacts in reconstructed CT images. The presence of ring artifact may compromise the accuracy of CT values and even introduce pseudo-structures, thereby reducing the application value of CT images. In this paper, we propose a dual-domain optimization model that takes advantage of the distribution characteristics of stripe artifact in 3D projection data and the prior features of reconstructed 3D CT images. Specifically, we demonstrate that stripe artifact in 3D projection data exhibit both group sparsity and low-rank properties. Building on this observation, we propose a TLT (TV-l2,1- Tucker) model to eliminate ring artifact in PCD-based cone beam CT (CBCT). In addition, an efficient iterative algorithm is designed to solve the proposed model. The effectiveness of both the model and the algorithm is evaluated through simulated and real data experiments. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art approaches.
Qin et al. (Thu,) studied this question.