Sparse reconstruction based on dictionary learning and group structure strategy for cone-beam X-ray luminescence computed tomography

As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.& COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is a promising dual-modal imaging technology for early tumor detection. However, the low absorption and high scattering of light in tissues pose challenges in CB-XLCT reconstruction. A proposed strategy using dictionary learning and group structure (DLGS) effectively addresses these challenges and achieves superior CB-XLCT reconstruction performance. Experimental results demonstrate the method's accuracy, target shape, robustness, dual-source resolution, and in vivo applicability.