Hyperspectral Neutron CT with Material Decomposition

TL;DR

This paper introduces a novel energy-resolved neutron imaging technique that enables isotopic material decomposition and 3D tomography with improved computational efficiency and robustness against noise.

Contribution

It presents a new method combining background estimation and sparse coding for rapid, semi-quantitative isotopic neutron CT imaging.

Findings

Achieved significant reduction in computation time from weeks to hours.

Demonstrated effective noise handling in low-count neutron measurements.

Enabled semi-quantitative, user-friendly isotopic imaging and 3D reconstruction.

Abstract

Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. The extremely high noise due to low count measurements is overcome using a sparse coding approach. It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.