Coded aperture X-ray diffraction (coherent scatter spectral) imaging provides fast and dose-efficient measurements of the molecular structure of an object. The information provided is spatially-dependent and material-specific, and can be utilized in medical applications requiring material discrimination, such as tumor imaging.
However, current coded aperture coherent scatter spectral imaging system assume a uniformly or weakly attenuating object, and are plagued by image degradation due to non-uniform self-attenuation. We propose accounting
for such non-uniformities in the self-attenuation by utilizing an X-ray computed tomography (CT) image (reconstructed attenuation map). In particular, we present an iterative algorithm for coherent scatter spectral image
reconstruction, which incorporates the attenuation map, at different stages, resulting in more accurate coherent
scatter spectral images in comparison to their uncorrected counterpart. The algorithm is based on a spectrally
grouped edge-preserving regularizer, where the neighborhood edge weights are determined by spatial distances
and attenuation values.