Cooperative data fusion of transmission and surface scan for improving limited-angle computed tomography reconstruction

Limited-angle computed tomography allows faster inspection during production, but the reconstruction from limited-angle transmission data is an underdetermined problem which cannot be solved without any prior knowledge of the sample. In this paper, surface data from an optical scan is selected as prior information due to its high accuracy and availability. To incorporate this information, we have developed a new cooperative data fusion model in the compressed sensing framework. The model has been applied to numerical and experimental data and solved with a tailored algorithm. We demonstrate the benefit of the data fusion model and prove the robustness of the algorithm. The results from this study indicate that the data fusion process combines features resolved by both modalities and gives a significant increase in image quality. These improvements enable metrological measurements that are impossible with data acquired with any single modality.