# Speckle-Based Transmission and Dark-Field Imaging for Material Analysis with a Laboratory X-Ray Source

## TL;DR

This paper explores how speckle-based X-ray imaging techniques can enhance material analysis in laboratory settings by providing complementary insights beyond traditional imaging methods.

## Contribution

The study confirms the feasibility and usefulness of speckle-based transmission and dark-field imaging for material analysis using a laboratory X-ray setup.

## Key findings

- The unified modulated pattern analysis method is the most reliable for recovering transmission and dark-field images.
- Transmission image contrast-to-noise ratio decreases with increasing object-to-detector distance due to noise.
- Absorption coefficients from transmission images are consistent across different experimental setups, making them useful for material discrimination.

## Abstract

Multimodal imaging is valuable because it can provide additional information beyond that obtained from a conventional bright-field (BF) image and can be implemented with a widely available device. In this paper, we investigate the implementation of speckle-based transmission (T) and dark-field (DF) imaging in a laboratory X-ray setup to confirm its usefulness for material analysis. Three methods for recovering T and DF images were applied to a sample composed of six materials: plastic, nylon, cardboard, cork, expanded polystyrene and foam with different absorption and scattering properties. Contrast-to-noise ratio (CNR) and linear attenuation, absorption and diffusion coefficients obtained from BF, T and DF images are studied for two object-to-detector distances (ODDs). Two analysis windows are evaluated to determine the impact of noise on the image contrast of T and DF images and the ability to retrieve material characteristics. The unified modulated pattern analysis method proves to be the most reliable among the three studied speckle-based methods. The results showed that the CNR of T and DF images increases with larger analysis windows, while linear absorption and diffusion coefficients remain constant. The CNR of T images decreases with increasing ODD due to noise, whereas the CNR of DF images exhibits more complex behaviour, due to the material-dependent reduction in DF signal with increasing ODD. The experimental results on the ODD dependence of T and DF signals are consistent with recently reported numerical simulation results of these signals. The absorption coefficients derived from T images are largely independent of the ODD and the speckle-based method used, making them a universal parameter for material discrimination. In contrast, the linear diffusion coefficients vary with the ODD, limiting their applicability to specific experimental configurations despite their notable advantages in distinguishing materials. These findings highlight that T and DF images obtained from a laboratory X-ray setup offer complementary insights, enhancing their value for material analysis.