Projection Embedded Diffusion Bridge for CT Reconstruction from Incomplete Data

TL;DR

This paper introduces PEDB, a novel diffusion model that explicitly incorporates data consistency for improved CT image reconstruction from incomplete projection data, outperforming existing methods across various scenarios.

Contribution

PEDB is the first diffusion bridge model to embed projection data into the score function, explicitly ensuring data consistency in CT reconstruction from incomplete data.

Findings

PEDB outperforms state-of-the-art models in sparse-view, limited-angle, and truncated projection scenarios.

PEDB achieves superior reconstruction quality under noisy and domain-shift conditions.

The method effectively balances stochasticity and data fidelity through a novel reverse SDE discretization scheme.

Abstract

Reconstructing CT images from incomplete projection data remains challenging due to the ill-posed nature of the problem. Diffusion bridge models have recently shown promise in restoring clean images from their corresponding Filtered Back Projection (FBP) reconstructions, but incorporating data consistency into these models remains largely underexplored. Incorporating data consistency can improve reconstruction fidelity by aligning the reconstructed image with the observed projection data, and can enhance detail recovery by integrating structural information contained in the projections. In this work, we propose the Projection Embedded Diffusion Bridge (PEDB). PEDB introduces a novel reverse stochastic differential equation (SDE) to sample from the distribution of clean images conditioned on both the FBP reconstruction and the incomplete projection data. By explicitly conditioning on the projection data in sampling the clean images, PEDB naturally incorporates data consistency. We embed the projection data into the score function of the reverse SDE. Under certain assumptions, we derive a tractable expression for the posterior score. In addition, we introduce a free parameter to control the level of stochasticity in the reverse process. We also design a discretization scheme for the reverse SDE to mitigate discretization error. Extensive experiments demonstrate that PEDB achieves strong performance in CT reconstruction from three types of incomplete data, including sparse-view, limited-angle, and truncated projections. For each of these types, PEDB outperforms evaluated state-of-the-art diffusion bridge models across standard, noisy, and domain-shift evaluations.