In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression–guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis.

在实际的计算机断层扫描 (CT) 应用中，由于辐射剂量的减少或设备限制，经常会遇到信噪比 (SNR) 低的投影。在这些情况下，经典的重建算法，如同步代数重建技术 (SART)，无法重建高质量的 CT 图像。基于块匹配和 3D 滤波 (BM3D) 的迭代重建算法 (POCS-BM3D) 在处理来自噪声投影的 CT 重建方面效果显着。然而，在低信噪比 CT 重建的情况下，BM3D 可能会抑制细节丢失过多的噪声。为了在噪声抑制和边缘保留之间实现更好的权衡，我们将引导图像滤波 (GIF) 引入低信噪比 CT 重建，并提出噪声抑制引导的图像滤波重建（NSGIFR）算法。在 NSGIFR 的每次迭代中，SART 的输出图像保留了更多的细节，作为 GIF 的输入图像，而 BM3D 去噪后的图像作为 GIF 的引导图像。实验结果表明，该算法在低信噪比 CT 重建中在保留结构和抑制噪声方面表现出优异的性能。NSGIFR在视觉效果和定量分析方面可以实现比SART、POCS-TV和POCS-BM3D更优越的图像质量。实验结果表明，该算法在低信噪比 CT 重建中在保留结构和抑制噪声方面表现出优异的性能。NSGIFR在视觉效果和定量分析方面可以实现比SART、POCS-TV和POCS-BM3D更优越的图像质量。实验结果表明，该算法在低信噪比 CT 重建中在保留结构和抑制噪声方面表现出优异的性能。NSGIFR在视觉效果和定量分析方面可以实现比SART、POCS-TV和POCS-BM3D更优越的图像质量。