Fast Hyperspectral Neutron Tomography

TL;DR

This paper introduces two novel algorithms for hyperspectral neutron tomography that significantly reduce reconstruction time and improve image quality by using subspace decomposition techniques.

Contribution

The paper presents fast hyperspectral reconstruction and material decomposition algorithms based on subspace decomposition, enhancing efficiency and accuracy over conventional methods.

Findings

Reduced computation time for hyperspectral neutron tomography

Improved image quality and noise reduction

Validated on both simulated and real data

Abstract

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.