Material-separating regularizer for multi-energy X-ray tomography

TL;DR

This paper introduces a novel regularizer for dual-energy X-ray tomography that effectively separates two materials in images, outperforming existing methods in pixel classification accuracy.

Contribution

A new regularizer combining non-negativity and an inner product penalty is proposed for improved material separation in dual-energy tomography.

Findings

Outperforms baseline joint total variation regularization

Effective in correctly identifying material pixels

Applicable to 3D and multiple materials with matching energies

Abstract

Dual-energy X-ray tomography is considered in a context where the target under imaging consists of two distinct materials. The materials are assumed to be possibly intertwined in space, but at any given location there is only one material present. Further, two X-ray energies are chosen so that there is a clear difference in the spectral dependence of the attenuation coefficients of the two materials. A novel regularizer is presented for the inverse problem of reconstructing separate tomographic images for the two materials. A combination of two things, (a) non-negativity constraint, and (b) penalty term containing the inner product between the two material images, promotes the presence of at most one material in a given pixel. A preconditioned interior point method is derived for the minimization of the regularization functional. Numerical tests with digital phantoms suggest that the new algorithm outperforms the baseline method, Joint Total Variation regularization, in terms of correctly material-characterized pixels. While the method is tested only in a two-dimensional setting with two materials and two energies, the approach readily generalizes to three dimensions and more materials. The number of materials just needs to match the number of energies used in imaging.