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

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. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this study, we proposed an optimized prior knowledge method for CB-XLCT imaging, which utilized a non-convex optimization model and an iterative algorithm to obtain a group of sparse solutions. The reasonable permissible source region (PSR) was achieved by intersecting a series of permissible regions (PRs), which enhanced the quality of inverse reconstruction.