{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36319",
  "author_total": 5,
  "authors": [
    {
      "name": "Hang Yu"
    },
    {
      "name": "Mojtaba Safari"
    },
    {
      "name": "Shansong Wang"
    },
    {
      "name": "Mingzhe Hu"
    },
    {
      "name": "Xiaofeng Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/17c99ekvyfm95.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36319",
  "fulltext_url": null,
  "id": "17c99ekvyfm95",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36319",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2204|Advanced X-ray and CT Imaging"
    ]
  ],
  "openalex": "W7224094082",
  "pdf_url": "https://arxiv.org/pdf/2610.36319",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/17c99ekvyfm95.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36319",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36319"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Image-Domain CT Metal Artifact Reduction Using an Endpoint-Conditioned Diffusion Bridge",
  "title_text": "Image-Domain CT Metal Artifact Reduction Using an Endpoint-Conditioned Diffusion Bridge",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/17c99ekvyfm95",
  "venue": "arXiv",
  "versions": []
}