Image-Domain CT Metal Artifact Reduction Using an Endpoint-Conditioned Diffusion Bridge
Background:
Metal implants cause streaking and shading artifacts in computed tomography (CT), obscuring anatomy and distorting Hounsfield-unit (HU) values. Conventional diffusion models reconstruct images from Gaussian noise rather than directly exploiting anatomical information retained in artifact-corrupted CT.
Purpose:
To develop an image-domain metal artifact reduction (MAR) method connecting artifact-corrupted and metal-free CT images through an endpoint-conditioned diffusion bridge.
Methods:
The proposed RDBM-MAR method uses an endpoint-conditioned diffusion bridge and initializes reconstruction from the artifact CT image. A conditional U-Net predicts an image-domain correction from intermediate bridge states. Inference uses 10 deterministic reverse steps. We used 14,000 paired CT slices from the AAPM CT-MAR Grand Challenge, including 12,045 training, 633 validation, and 1,322 held-out test slices. One clinical benchmark case was assessed qualitatively. We compared RDBM-MAR with CycleGAN, Pix2Pix, Res-SRDiff, I2I-Mamba, and I2SB using PSNR, SSIM, MAE, and LPIPS. Statistical analysis used Friedman and paired Wilcoxon signed-rank tests with Holm correction.
Results:
RDBM-MAR achieved the highest PSNR (43.67 +/- 4.56 dB) and SSIM (0.9856 +/- 0.0138) and the lowest MAE (14.74 +/- 8.68 HU). All four metrics differed significantly among methods (p < 0.001). RDBM-MAR outperformed all five comparators in PSNR, SSIM, and MAE (adjusted p < 0.001); Res-SRDiff achieved the lowest LPIPS. Difference maps, HU profiles, and the clinical case showed reduced artifacts with preservation of surrounding anatomy.
Conclusions:
RDBM-MAR provides a direct restoration trajectory from artifact CT toward metal-free CT, achieving strong structural fidelity and HU accuracy using 10 reverse steps.
