Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Area of Science:

• Materials Science
• Neutron Imaging
• Computational Imaging

Background:

• Wavelength-resolved neutron tomography (WRNT) is crucial for 3D materials characterization.
• Standard WRNT methods suffer from noisy data and long acquisition times, leading to artifacts.
• Limited experimental time and sample constraints exacerbate these issues.

Purpose of the Study:

• To develop a novel acquisition and reconstruction strategy for high-quality WRNT.
• To reduce artifacts and accelerate the feedback loop in WRNT experiments.
• To provide guidelines for designing advanced WRNT systems.

Main Methods:

• Implemented an interlaced scanning technique for diverse data acquisition.
• Combined interlaced scanning with a model-based image reconstruction algorithm.
• Utilized a cost-function minimization approach with data-fidelity and regularization terms.

Main Results:

• Demonstrated high-quality WRNT reconstructions concurrent with data acquisition.
• Successfully produced artifact-reduced images from noisy experimental data.
• Showcased the technique's effectiveness on a magnetite sample over two days.

Conclusions:

• The proposed interlaced scanning and model-based reconstruction significantly improve WRNT quality and speed.
• This combined approach offers a viable solution for real-time, high-fidelity 3D material characterization.
• The findings provide a novel framework for future WRNT system development.