Material Identification From Radiographs Without Energy Resolution

TL;DR

This paper introduces a method for identifying materials in radiographs without energy resolution, enabling standard X-ray systems to determine material types efficiently and accurately without additional hardware.

Contribution

The authors develop a combinatorial optimization approach with a branch and bound algorithm for material identification without energy-resolved measurements, applicable to real-world security imaging.

Findings

Accurate material identification achieved in synthetic and real data.

Algorithm runs in six minutes on a consumer laptop for scenes with multiple materials.

Method outperforms traditional techniques requiring specialized hardware.

Abstract

We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical-requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications-thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.