Abstract
Photon-counting detector (PCD) can identify absorption features in the multiple ranges of photon energies, which has a great potential in material discrimination. In this paper, we focused on in vivo dual-energy CT imaging to characterize different biomedical compositions. The precision of material decomposition in post-reconstruction space depends on the quality of reconstructed CT images; we used the locally linear embedding (LLE) based online geometric calibration method and GPU-based reconstruction toolbox to reconstruct high-quality CT images. Then, we performed the real experiment and studied materials decomposition with basis material model to discriminate soft tissue and cortical bone of small animal. Finally, the experimental results demonstrated that the proposed method could reconstruct small animal CT images with more slim structures and details, and improve the precision of materials decomposition in dual-energy CT imaging.