论文｜Xiang Chen, Wenjun Xia, Ziyuan Yang, Hu Chen, Yan Liu, Jiliu Zhou, and Yi Zhang*：SOUL-Net: A sparse and low-rank unrolling network for spectral CT image reconstruction

本文（SOUL-Net: A sparse and low-rank unrolling network for spectral CT image reconstruction）原载IEEE Transactions on Neural Networks and Learning Systems，由四川大学张意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) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4-D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires a manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this article, we propose a sparse and low-rank unrolling network (SOUL-Net) for spectral CT image reconstruction, that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction. 

Xiang Chen, Wenjun Xia, Ziyuan Yang, Hu Chen, Yan Liu, Jiliu Zhou, and Yi Zhang*. SOUL-Net: A sparse and low-rank unrolling network for spectral CT image reconstruction. IEEE Transactions on Neural Networks and Learning Systems, online, DOI 10.1109/TNNLS.2023.3319408, 2023.（论文下载）