Background Sparse-view CT reduces radiation dose by decreasing the number of projections, yet the resulting undersampling introduces severe artifacts in images reconstructed with traditional analytical algorithms. Recent diffusion posterior sampling (DPS)-based methods enhance image quality but frequently generate spurious details and incur prohibitive computational cost, limiting clinical adoption. Objective To enable high-fidelity, low-dose CT imaging from sparse projections while suppressing hallucinated details and reducing computational burden. Methods We propose a novel diffusion-based method that synergizes null-space restoration with Filtered Back-Projection (FBP) pseudoinverse approximation. Specifically, by employing range–null space decomposition, we use diffusion models to restore null-space image components while ensuring data consistency through the FBP algorithm approximating the pseudoinverse of projection matrix in the range image space. Moreover, we provide a theoretical analysis of the rationality of this approximation. This novel approach effectively combines the strengths of diffusion models and traditional CT reconstruction techniques, optimizing the inverse diffusion trajectory to enable high-fidelity image recovery from sparse data. Results Experimental results show that the proposed method achieves significant improvements in image quality and computational efficiency. Compared with the DPS method, it yields an average PSNR gain of 5.32 dB, an average SSIM increase of 0.083, and a 41.9% reduction in computation time. Conclusion In summary, this framework provides a practical and effective solution for high-quality, low-dose CT imaging, effectively balancing reconstruction accuracy and computational efficiency in practical applications.
Xing et al. (Tue,) studied this question.