Abstract
Dual-energy imaging is a clinically well-established technique that offers several advantages over conventional X-ray imaging. By performing measurements with two distinct X-ray spectra, differences in energy-dependent attenuation are exploited to obtain material-specific information. This information is used in various imaging applications to improve clinical diagnosis. In recent years, grating-based X-ray dark-field imaging has received increasing attention in the imaging community. The X-ray dark-field signal originates from ultra small-angle scattering within an object and thus provides information about the microstructure far below the spatial resolution of the imaging system. This property has led to a number of promising future imaging applications that are currently being investigated. However, different microstructures can hardly be distinguished with current X-ray dark-field imaging techniques, since the detected dark-field signal only represents the total amount of ultra small-angle scattering. To overcome these limitations, we present a novel concept called dual-energy X-ray dark-field material decomposition, which transfers the basic material decomposition approach from attenuation-based dual-energy imaging to the dark-field imaging modality. We develop a physical model and algorithms for dual-energy dark-field material decomposition and evaluate the proposed concept in experimental measurements. Our results suggest that by sampling the energy-dependent dark-field signal with two different X-ray spectra, a decomposition into two different microstructured materials is possible. Similar to dual-energy imaging, the additional microstructure-specific information could be useful for clinical diagnosis.
Highlights
T HE concept of dual-energy imaging, which was first introduced in 1976 [1], represents a great milestone in diagnostic X-ray imaging
Owing to the high transmittance of the absorption gratings around the K-edge of gold, the reference visibility is strongly reduced around 80 keV which results in a poor signal-to-noise ratio of the extracted data in the corresponding range
The smaller differences in the energy-dependent dark-field signal aggravate the degradation of the signal-to-noise ratio (SNR), since the SNR is directly proportional to how much the ratio of the average linear diffusion coefficients for the low and high energy spectrum differs between the two basis materials
Summary
T HE concept of dual-energy imaging, which was first introduced in 1976 [1], represents a great milestone in diagnostic X-ray imaging. Owing to its capability of extracting material-specific information non-invasively from an object, dual-energy imaging initiated the development of several clinical applications. Dual-energy computed tomography (DECT) has become a powerful and well-established diagnostic tool in the daily clinical workflow. DECT has turned out to be valuable for abdominal imaging, where it has led to significant improvement in diagnostic imaging. The availability of additional information in the form of virtual monochromatic images and quantitative material-specific contrast agent density maps can provide an improved detectability of oncological [2], [3] and vascular [4], [5] pathologies. Apart from DECT, there are several dual-energy applications relying on projection data only, such as dual-energy X-ray absorptiometry [6], dual-energy subtraction radiography [7] and contrast-enhanced digital mammography [8]
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