This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse problems. Spectral CT and more particularly photon-counting CT (PCCT) can perform transmission measurements at different energy levels which can be used for material decomposition. It is an ill-posed inverse problem and therefore requires regularization. DMs are a class of generative model that can be used to solve inverse problems via diffusion posterior sampling (DPS). In this paper we adapt DPS for material decomposition in a PCCT setting. We propose two approaches, namely Two-step Diffusion Posterior Sampling (TDPS) and One-step Diffusion Posterior Sampling (ODPS). Early results from an experiment with simulated low-dose PCCT suggest that DPSs have the potential to outperform state-of-the-art model-based iterative reconstruction (MBIR). Moreover, our results indicate that TDPS produces material images with better peak signal-to-noise ratio (PSNR) than images produced with ODPS with similar structural similarity (SSIM).
@article{arxiv.2403.10183,
title = {Spectral CT Two-step and One-step Material Decomposition using Diffusion Posterior Sampling},
author = {Corentin Vazia and Alexandre Bousse and Jacques Froment and Béatrice Vedel and Franck Vermet and Zhihan Wang and Thore Dassow and Jean-Pierre Tasu and Dimitris Visvikis},
journal= {arXiv preprint arXiv:2403.10183},
year = {2024}
}