Electron tomography for sintered ceramic materials by a neural network algebraic reconstruction technique

Abstract

The missing wedge effect in electron tomography introduces various types of artifacts in the tomograms and lowers the reconstruction resolution and quality. The artifacts produced in tomographic reconstruction of bulk materials can be very severe, particularly for sintered bulk ceramic materials in which there are often nano-pores or pore-like microstructure features. Here, we report a neural network algebraic reconstruction algorithm with no prior knowledge to perform electron tomography for a sintered SiC material with nano carbon zones. The results show that the proposed algorithm has a great suppressive effect on the missing wedge artifacts and a high tolerance for noise. The information in the missing wedge can be partly recovered by this technique. Thus, both the shape of the bulk SiC specimen and its irregular inner pore-like features are correctly retrieved in the obtained 3D image. Our study shows the effectiveness of the neural network algorithm for improving the reconstruction accuracy of electron tomography, in order to reveal sophisticated 3D microstructures generally existing in sintered ceramic materials.