DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction
This addresses the problem of high-quality medical image reconstruction for healthcare applications, representing an incremental advance by combining existing methods in a novel way.
The paper tackles sparse-view CT reconstruction by introducing DICE, a framework that integrates diffusion models with consensus equilibrium to balance generative priors and measurement consistency, achieving significant improvements over state-of-the-art baselines for 15, 30, and 60 views out of 180.
Sparse-view computed tomography (CT) reconstruction is fundamentally challenging due to undersampling, leading to an ill-posed inverse problem. Traditional iterative methods incorporate handcrafted or learned priors to regularize the solution but struggle to capture the complex structures present in medical images. In contrast, diffusion models (DMs) have recently emerged as powerful generative priors that can accurately model complex image distributions. In this work, we introduce Diffusion Consensus Equilibrium (DICE), a framework that integrates a two-agent consensus equilibrium into the sampling process of a DM. DICE alternates between: (i) a data-consistency agent, implemented through a proximal operator enforcing measurement consistency, and (ii) a prior agent, realized by a DM performing a clean image estimation at each sampling step. By balancing these two complementary agents iteratively, DICE effectively combines strong generative prior capabilities with measurement consistency. Experimental results show that DICE significantly outperforms state-of-the-art baselines in reconstructing high-quality CT images under uniform and non-uniform sparse-view settings of 15, 30, and 60 views (out of a total of 180), demonstrating both its effectiveness and robustness.