  Scatter can account for large errors in cone-beam CT (CBCT) due to its wide
field of view, and its complicated nature makes its compensation difficult.
Iterative polyenergetic reconstruction algorithms offer the potential to
provide quantitative imaging in CT, but they are usually incompatible with
scatter contaminated measurements. In this work, we introduce a polyenergetic
convolutional scatter model that is directly fused into the reconstruction
process, and exploits information readily available at each iteration for a
fraction of additional computational cost. We evaluate this method with
numerical and real CBCT measurements, and show significantly enhanced electron
density estimation and artifact mitigation over pre-calculated fast adaptive
scatter kernel superposition (fASKS). We demonstrate our approach has two
levels of benefit: reducing the bias introduced by estimating scatter prior to
reconstruction; and adapting to the spectral and spatial properties of the
specimen.
