Sparse-view neutron CT 3D image reconstruction algorithm based on split Bregman method

Abstract

As a complement to X-ray computed tomography (CT), neutron tomography has been extensively used in nuclear engineering, materials science, cultural heritage, and industrial applications. Reconstruction of the attenuation matrix for neutron tomography with a traditional analytical algorithm requires hundreds of projection views in the range of 0° to 180° and typically takes several hours to complete. Such a low time-resolved resolution degrades the quality of neutron imaging. Decreasing the number of projection acquisitions is an important approach to improve the time resolution of images; however, this requires efficient reconstruction algorithms. Therefore, sparse-view reconstruction algorithms in neutron tomography need to be investigated. In this study, we investigated the three-dimensional reconstruction algorithm for sparse-view neutron CT scans. To enhance the reconstructed image quality of neutron CT, we propose an algorithm that uses OS-SART to reconstruct images and a split Bregman to solve for the total variation (SBTV). A comparative analysis of the performances of each reconstruction algorithm was performed using simulated and actual experimental data. According to the analyzed results, OS-SART-SBTV is superior to the other algorithms in terms of denoising, suppressing artifacts, and preserving detailed structural information of images.