论文|Wenjun Xia, Weiwen Wu, Shanzhou Niu, Fenglin Liu, Jiliu Zhou, Hengyong Yu, Ge Wang, and Yi Zhang. Spectral CT Reconstruction-ASSIST: Aided by Self-Similarity in Image-Spectral Tensors

本文原载IEEE Transactions on Computational Imaging，由四川大学计算机学院张意副教授等科研人员创作，系四川大学“智慧法治”超前部署学科系列学术成果。后续会持续分享四川大学“智慧法治”超前部署学科系列学术成果，欢迎大家阅读。

Abstract: Spectral computed tomography (CT) reconstructs multienergy images from data in different energy bins. However, these reconstructed images can be contaminated by noise due to the limited numbers of photons in the corresponding energy bins. In this paper, we propose a spectral CT reconstruction method aided by self-similarity in image-spectral tensors, which utilizes the selfsimilarity of patches in both spatial and spectral domains. Patches with similar structures identified by a joint spatial and spectral searching strategy form a basic tensor unit, and can be utilized to improve image quality. Specifically, each tensor is decomposed into a low-rank component and a sparse component, which respectively represent the stable structures and feature differences across different energy bins. The augmented Lagrange method is applied to optimize the proposed objective function. To validate the performance of the proposed method, several simulated clinical and real data experiments are performed. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of preserving image details and reducing artifacts.

Keywords: Spectral CT; low-rank decomposition; sparse representation; tensor

Wenjun Xia, Weiwen Wu, Shanzhou Niu, Fenglin Liu, Jiliu Zhou, Hengyong Yu, Ge Wang, and Yi Zhang. Spectral CT Reconstruction-ASSIST: Aided by Self-Similarity in Image-Spectral Tensors, IEEE Transactions on Computational Imaging, pp.420-436, vol.5, no.3, 2019.（论文下载）