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We propose a new modeling approach for scatter estimation and descattering inpolyenergetic X-ray computed tomography (CT) based on fitting models to localneighborhoods of a training set. X-ray CT is widely used in medical andindustrial applications. X-ray scatter, if not accounted for duringreconstruction, creates a loss of contrast in CT reconstructions and introducessevere artifacts including cupping, shading, and streaks. Even when thesequalitative artifacts are not apparent, scatter can pose a major obstacle inobtaining quantitatively accurate reconstructions. Our approach to estimatingscatter is, first, to generate a training set of 2D radiographs with andwithout scatter using particle transport simulation software. To estimatescatter for a new radiograph, we adaptively fit a scatter model to a smallsubset of the training data containing the radiographs most similar to it. Wecompared local and global (fit on full data sets) versions of several X-rayscatter models, including two from the recent literature, as well as a recentdeep learning-based scatter model, in the context of descattering andquantitative density reconstruction of simulated, spherically symmetrical,single-material objects comprising shells of various densities. Our resultsshow that, when applied locally, even simple models provide state-of-the-artdescattering, reducing the error in density reconstruction due to scatter bymore than half.

Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography