Rayleigh imaging in spectral mammography

TL;DR

This paper demonstrates that a modified spectral mammography system can acquire triple-energy images, enabling measurement of Rayleigh scattering and allowing for material decomposition with three basis materials, enhancing spectral imaging applications.

Contribution

It introduces a novel triple-energy imaging approach using a modified spectral mammography system to measure Rayleigh scattering for improved material decomposition.

Findings

Successful acquisition of three spectral images in mammography.

Rayleigh scattering measurement enhances material differentiation.

Potential to improve spectral imaging applications.

Abstract

Spectral imaging is the acquisition of multiple images of an object at different energy spectra. In mammography, dual-energy imaging (spectral imaging with two energy levels) has been investigated for several applications, in particular material decomposition, which allows for quantitative analysis of breast composition and quantitative contrast-enhanced imaging. Material decomposition with dual-energy imaging is based on the assumption that there are two dominant photon interaction effects that determine linear attenuation: the photoelectric effect and Compton scattering. This assumption limits the number of basis materials, i.e. the number of materials that are possible to differentiate between, to two. However, Rayleigh scattering may account for more than 10% of the linear attenuation in the mammography energy range. In this work, we show that a modified version of a scanning multi-slit spectral photon-counting mammography system is able to acquire three images at different spectra and can be used for triple-energy imaging. We further show that triple-energy imaging in combination with the efficient scatter rejection of the system enables measurement of Rayleigh scattering, which adds an additional energy dependency to the linear attenuation and enables material decomposition with three basis materials. Three available basis materials have the potential to improve virtually all applications of spectral imaging.