Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction

TL;DR

This paper introduces CvG-Diff, a novel diffusion model reformulation for sparse-view CT reconstruction that explicitly models artifacts and employs innovative training and sampling strategies, achieving high-quality results with fewer steps.

Contribution

The paper presents CvG-Diff, a generalized diffusion approach with error-propagation suppression and semantic-priorized sampling, improving efficiency and quality in sparse-view CT reconstruction.

Findings

Achieves 38.34 dB PSNR and 0.9518 SSIM with only 10 steps.

Outperforms state-of-the-art methods in sparse-view CT reconstruction.

Effectively models artifacts using a deterministic degradation operator.

Abstract

Sparse-view computed tomography (CT) reduces radiation exposure by subsampling projection views, but conventional reconstruction methods produce severe streak artifacts with undersampled data. While deep-learning-based methods enable single-step artifact suppression, they often produce over-smoothed results under significant sparsity. Though diffusion models improve reconstruction via iterative refinement and generative priors, they require hundreds of sampling steps and struggle with stability in highly sparse regimes. To tackle these concerns, we present the Cross-view Generalized Diffusion Model (CvG-Diff), which reformulates sparse-view CT reconstruction as a generalized diffusion process. Unlike existing diffusion approaches that rely on stochastic Gaussian degradation, CvG-Diff explicitly models image-domain artifacts caused by angular subsampling as a deterministic degradation operator, leveraging correlations across sparse-view CT at different sample rates. To address the inherent artifact propagation and inefficiency of sequential sampling in generalized diffusion model, we introduce two innovations: Error-Propagating Composite Training (EPCT), which facilitates identifying error-prone regions and suppresses propagated artifacts, and Semantic-Prioritized Dual-Phase Sampling (SPDPS), an adaptive strategy that prioritizes semantic correctness before detail refinement. Together, these innovations enable CvG-Diff to achieve high-quality reconstructions with minimal iterations, achieving 38.34 dB PSNR and 0.9518 SSIM for 18-view CT using only \textbf{10} steps on AAPM-LDCT dataset. Extensive experiments demonstrate the superiority of CvG-Diff over state-of-the-art sparse-view CT reconstruction methods. The code is available at https://github.com/xmed-lab/CvG-Diff.