Sparse-View Neutron Computed Tomography 3-D Reconstruction via the Fast Gradient Projection Algorithm

Abstract

Neutron tomography is an efficient nondestructive testing technique. As a complement to X-ray computed tomography, it has been widely used in various fields. Due to the difficulty of obtaining complete neutron projection data in a high-radiation environment and the high noise characteristics of neutron images, it is difficult to reconstruct a high-quality image using the conventional filtered-back projection (FBP) algorithm. Therefore, research on sparse-view reconstruction algorithms in neutron tomography is needed. To improve the quality of neutron three-dimensional reconstructed images, this paper proposes an algorithm that combines the Simultaneous Algebraic Reconstruction Technique (SART) with Fast Gradient Projection (FGP), where the FGP is an algorithm for image denoising and deblurring based on the discrete total variation (TV) minimization model. The algorithm proposed in this paper is compared with other algorithms (FBP, SART, and SART-TV) by simulated experimental data and real neutron experimental data. The experimental results show that the novel algorithm outperforms the other three algorithms in terms of denoising and retaining detailed structural information.

Keywords:

• Sparse-view
• neutron computed tomography
• three-dimensional reconstruction
• fast gradient-projection
• SART

Author Contributions

Conceptualization, Y. L. and X. O.; methodology, Y. L. and Z. T.; software, Z. T. and Y. L.; validation, Z. T. and Y. L.; formal analysis, Z. T. and Y. L.; investigation, Z. T. and Y. L.; resources, Y. L.; data curation, Z. T.; writing—original draft preparation, Z. T. and Y. L.; writing—review and editing, Y. L. and Z. T., visualization, Z. T.; supervision, Y. L.; project administration, and Y. L.; funding acquisition, Y. L. All authors have read and agreed to the published version of the paper.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [no. 2022YFB1902700], the Joint Fund of Ministry of Education for Equipment Pre-research [no. 8091B042203], the National Natural Science Foundation of China [no. 11875129), the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect [no. SKLIPR1810], the Fund of Innovation Center of Radiation Application [no. KFZC2020020402], the Fund of the State Key Laboratory of Nuclear Physics and Technology, Peking University [no. NPT2023KFY06], the Joint Innovation Fund of China National Uranium Co., Ltd., State Key Laboratory of Nuclear Resources and Environment, East China University of Technology [no. 2022NRE-LH-02], and the Fundamental Research Funds for the Central Universities[no. 2023JG001].