Abstract: Sparse-view Cone-beam X-ray luminescence computed tomography (CB-XLCT) is a novel multimode optical tomography technique, which has shown good potential for real-time detection of early tumor. However, the inverse problem of sparse-view CB-XLCT is more serious than that of traditional multi-angle CB-XLCT, because of the limited projective data. Aiming at above problem, this paper proposes a spectral regression (SR) and preconditioning method to improve multispectral reconstruction for sparse-view CB-XLCT. Firstly, the multispectral strategy is used to construct the system matrix and model the inverse problem; Then, the spectral regression method is used to learn the features of the high-dimensional system matrix in the previous inverse problem; After that, a preconditioning approach is used to reduce the coherence of the new system matrix, and further form a new inverse problem. Numerical simulations and robustness tests were performed to verify the effectiveness and robustness of the proposed method. The experimental results indicated that our proposed method not only can significantly improve the imaging quality of multispectral reconstruction for sparse-view CB-XLCT, but also has good robutsness.

Key words: biotechnology, X-ray luminescence computed tomography, 3D reconstruction, multispectral strategy