  Using recent advances in generative artificial intelligence (AI) brought by
diffusion models, this paper introduces a new synergistic method for spectral
computed tomography (CT) reconstruction. Diffusion models define a neural
network to approximate the gradient of the log-density of the training data,
which is then used to generate new images similar to the training ones.
Following the inverse problem paradigm, we propose to adapt this generative
process to synergistically reconstruct multiple images at different energy bins
from multiple measurements. The experiments suggest that using multiple energy
bins simultaneously improves the reconstruction by inverse diffusion and
outperforms state-of-the-art synergistic reconstruction techniques.
