  We present a method for descattering and quantitative density reconstruction
in polyenergetic X-ray computed tomography (CT) based on fitting local models
of scatter. X-ray CT is widely used in medical and industrial applications. If
not accounted for during reconstruction, X-ray scatter creates a loss of
contrast and introduces severe image artifacts including cupping, shading, and
streaks. Even when these qualitative artifacts are not apparent, scatter poses
a major obstacle in obtaining accurate quantitative radiographic
reconstructions. Our approach to estimating scatter is to generate a training
set of radiographs with and without scatter using particle transport simulation
software. We then learn a locally adaptive model, i.e., one comprised of many
models, each fit to a local neighborhood of the training data. We use this
scatter model inside an iterative descattering algorithm and reconstruct
densities from the corrected data. Our experiments on monoenergetic and
polyenergetic data show that, when applied locally, even simple, linear models
are highly-effective at estimating scatter. Further, descattering approaches
based on these local models can reduce the effect of scatter on density
reconstruction by more than half.
