Joint reconstruction and scatter estimation in cone-beam CT using diffusion posterior sampling

X-ray scatter degrades image quality in computed tomography, particularly in cone-beam CT due to wide cone angles. While mono-energetic CT scatter correction is well-studied, spectral CT imaging presents additional challenges due to its sensitivity to unmodeled biases in material decomposition and density estimation. This work presents a joint estimation approach that simultaneously estimates scatter and material densities by integrating the scatter component into a spectral CT forward model. Using Diffusion Posterior Sampling method, we leverage the combination of prior knowledge from large dataset training and the physical model for joint density and scatter estimation. Tested on simulated and phantom data, our method significantly reduce artifacts associated with unestimated scatter, improving spectral CT image quality.