Optimizing the Reduction of Streaking Artifacts in Routine Non-Contrast Chest CT with a Guided Diffusion Deep Learning Method

This retrospective study introduces a guided diffusion deep learning method to mitigate streaking artifacts in chest CT scans caused by scanner issues, physical effects, patient factors, and helical and multisection techniques. Data from 763 non-contrast CT series (110–140 kVp, 165 mAs average, 0.5–10 mm slices) across four centers included 47,032 artifact-affected and 49,609 artifact-free slices. The model was trained by concatenating artifact-free CT images with segmentation masks and anatomical ROIs to preserve structures during the diffusion process. Artifact-laden images were processed through the trained model to generate artifact-free outputs. Comparing samples with artifacts and those without from four centers, a statistically significant difference in SNR and CNR of anatomical ROIs (p<0.05) was observed. The generated images demonstrated high consistency with actual artifact-free samples, with lung field SNR values of 26.67±2.01 and 26.11±1.89, and CNR between lung fields and trachea of 3.76±0.77 and 3.78±0.56.  Results showed enhanced performance over CycleGAN and other diffusion models with SSIM 0.863±0.01 and PSNR 36.952±0.67(p<0.05), achieving high DSC for anatomical consistency. Findings demonstrate effective artifact reduction while maintaining structural integrity, offering potential clinical value in diagnostic accuracy and image quality enhancement.