With the help of the clinical application of CLI in tumour and lymph node imaging, Cerenkov luminescence tomography (CLT) has the potential to be used for cancer staging. If staging cancer based on optical image of tumour, node and metastasis, one of the critical issues is multiple-source resolution. Because of the ill-posedness of the inverse problem and the diversity of tumor biological characteristics, the multiple-source resolution is a meaningful but challenge problem. In this paper, based on the compression perception theory, a non-convex sparse regularization algorithm (nCSRA) framework was proposed to improve the capacity of multiple-source resolving. Two typical algorithms (homotopy and iterative shrinkage-thresholding algorithm) were explored to test the performance of nCSRA. In numerical simulations and in vivo imaging experiments, the comparison results showed that the proposed nCSRA framework can significantly enhance the multiple-source resolution capability in aspect of spatial resolution, intensity resolution, and size resolution.

Fluorescence molecular tomography (FMT) is a promising imaging technique that allows in vivo visualization of molecular-level events associated with disease progression and treatment response. Accurate and efficient 3D reconstruction algorithms will facilitate the wide-use of FMT in preclinical research. Here, we utilize L_1/2-norm regularization for improving FMT reconstruction. To efficiently solve the nonconvex L_1/2-norm penalized problem, we transform it into a weighted L₁-norm minimization problem and employ a homotopy-based iterative reweighting algorithm to recover small fluorescent targets. Both simulations on heterogeneous mouse model and in vivo experiments demonstrated that the proposed L_1/2-norm method outperformed the comparative L₁-norm reconstruction methods in terms of location accuracy, spatial resolution and quantitation of fluorescent yield. Furthermore, simulation analysis showed the robustness of the proposed method, under different levels of measurement noise and number of excitation sources.

Fluorescent molecular tomography (FMT) is a highly sensitive and noninvasive imaging approach for providing three-dimensional distribution of fluorescent marker probes. However, owing to its light scattering effect and the ill-posedness of inverse problems, it is challenging to develop an efficient reconstruction algorithm that can achieve the exact location and morphology of the fluorescence source. In this study, therefore, in order to satisfy the need for early tumor detection and improve the sparsity of solution, we proposed a novel L₁-L₂ norm regularization via the forward-backward splitting method for enhancing the FMT reconstruction accuracy and the robustness. By fully considering the highly coherent nature of the system matrix of FMT, it operates by splitting the objective to be minimized into simpler functions, which are dealt with individually to obtain a sparser solution. An analytic solution of L₁-L₂ norm proximal operators and a forward-backward splitting algorithm were employed to efficiently solve the nonconvex L₁-L₂ norm minimization problem. Numerical simulations and an in-vivo glioma mouse model experiment were conducted to evaluate the performance of our algorithm. The comparative results of these experiments demonstrated that the proposed algorithm obtained superior reconstruction performance in terms of spatial location, dual-source resolution, and in-vivo practicability. It was believed that this study would promote the preclinical and clinical applications of FMT in early tumor detection.