Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it is important for early detection of diseases, targeted therapy and drug development. However, the columns of system matrix used for CB-XLCT imaging tend to be highly coherent, which means L1 minimization may not produce the sparsest solution. A novel reconstruction method by minimizing the difference between L1 and L2 norms is proposed. To solve the non-convex L1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. The performance of the proposed method is investigated with simulated data and in vivo experimental data. The results demonstrate that the DCA for L1-2 minimization outperforms the representative algorithms for L1, L2, L1/2, TV and L0 when the system matrix is highly coherent. The proposed method can solve the rapid imaging problem of CB-XLCT effectively.