OPK_SNCA: Optimized prior knowledge via sparse non-convex approach for cone-beam X-ray luminescence computed tomography imaging. (March 2022)

• Record Type: Journal Article
• Authors: Zhang, Haibo; Hai, Linqi; Kou, Jiaojiao; Hou, Yuqing; He, Xiaowei; Zhou, Mingquan; Geng, Guohua
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106645
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: OPK_SNCA was proposed to alleviate the ill-posedness of inverse problem of CB-XLCT imaging. Unlike a traditionally subjective determination or artificial threshold, a sparse non-convex approach was employed to obtain an accurate and stable permissible source region without manual intervention. The proposed OPK_SNCA is not only suitable for combining with different types of algorithms for CB-XLCT, but also easy to extend to other imaging modalities. Abstract: Background: The development of Cone-beam X-ray luminescence computed tomography (CB-XLCT) has allowed the quantitative in-depth biological imaging, but with a greatly ill-posed and ill-conditioned inverse problem. Although the predefined permissible source region (PSR) is a widely used way to alleviate the problem for CB-XLCT imaging, how to obtain the accurate PSR is still a challenge for the process of inverse reconstruction. Methods: We proposed an optimized prior knowledge via a sparse non-convex approach (OPK_SNCA) for CB-XLCT imaging. Firstly, non-convex Lp -norm optimization model was employed for copying with the inverse problem, and an iteratively reweighted split augmented lagrangian shrinkage algorithm was developed to obtain a group of sparse solutions based on different non-convex p values. Secondly, a series of permissible regions (PRs) with different discretized mesh was further achieved, and the intersection operation was implemented on the group of PRs to get a reasonable PSR. After that, the final PSR was adopted as an optimized prior knowledge to enhance the reconstruction quality of inverse reconstruction. Results: Both simulation experiments and in vivo experiment were performed to evaluate the efficiency and robustness of the proposed method. Conclusions: The experimental results demonstrated that our proposed method could significantly improve the imaging quality of the distribution of X-ray-excitable nanophosphors for CB-XLCT.