Sparse non-convex L_p regularization for cone-beam X-ray luminescence computed tomography

ABSTRACT

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex L_p (0 < p < 1) regularization was utilized for the efficient reconstruction for early detection of small tumour in CB-XLCT imaging. Specifically, we transformed the non-convex optimization problem into an iteratively reweighted scheme based on the L₁ regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-L_p) was proposed to efficiently solve the non-convex L_p (0 < p < 1) model. We studied eight different non-convex p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L₂ and L₁ regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our L_p(1/4 < p < 1/2) methods give the best performance.

KEYWORDS:

• Cone-beam X-ray luminescence computed tomography
• image reconstruction techniques
• optical tomography
• X-ray imaging
• medical and biological imaging

Acknowledgment

The authors would like to thank the School of Life Science and Technology of Xidian University for providing experimental data acquisition system.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61772421, 11571012, 61731015, 61673319, 61640418, 61601363], Scientific research plan projects of Shaanxi Education Department of China [grant numbers 17JF027, 16JK1775].