Ensemble Learning of Diffusion Model for Sparse-View CT Reconstruction

Abstract

Inspired by the success of diffusion models in natural image processing, researches on medical image processing using diffusion models have become increasingly active in recent years. In particular, using pre-trained diffusion models as priors for medical image reconstruction is currently one of the most actively studied topics in this field. When incorporating diffusion models into medical image reconstruction, it is crucial to balance the fidelity to the observed data (e.g., projection data) and the diffusion prior. In this study, we propose a medical image reconstruction method based on an ensemble of diffusion models that automatically optimizes this balance, and we apply the proposed method to sparse-view CT image reconstruction.