First-order primal–dual algorithm for sparse-view neutron computed tomography-based three-dimensional image reconstruction

Neutron computed tomography (NCT) is widely used as a noninvasive measurement technique in nuclear engineering, thermal hydraulics, and cultural heritage. The neutron source intensity of NCT is usually low and the scan time is long, resulting in a projection image containing severe noise. To reduce the scanning time and increase the image reconstruction quality, an effective reconstruction algorithm must be selected. In CT image reconstruction, the reconstruction algorithms can be divided into three categories: analytical algorithms, iterative algorithms, and deep learning. Because the analytical algorithm requires complete projection data, it is not suitable for reconstruction in harsh environments, such as strong radiation, high temperature, and high pressure. Deep learning requires large amounts of data and complex models, which cannot be easily deployed, as well as has a high computational complexity and poor interpretability. Therefore, this paper proposes the OS-SART-PDTV iterative algorithm, which uses the ordered subset simultaneous algebraic reconstruction technique (OS-SART) algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation (PDTV), for sparse-view NCT three-dimensional reconstruction. The novel algorithm was compared with other algorithms (FBP, OS-SART-TV, OS-SART-AwTV, and OS-SART-FGPTV) by simulating the experimental data and actual neutron projection experiments. The reconstruction results demonstrate that the proposed algorithm outperforms the FBP, OS-SART-TV, OS-SART-AwTV, and OS-SART-FGPTV algorithms in terms of preserving edge structure, denoising, and suppressing artifacts.