{
  "abstract": "Purpose:: Accurate needle tracking is critical for the success of computed tomography (CT)-guided interventions, where even minor deviations may compromise procedural safety and clinical outcomes. However, existing image-guided tracking systems typically lack mechanisms to quantify and communicate the reliability of their predictions in real time, leaving clinicians to act on guidance of uncertain trustworthiness.\n\nMethods:: We propose a Dynamic Uncertainty Level Assessment Framework that provides a quantitative, real-time estimate of tracking reliability by linearly linking a predicted uncertainty score to spatial tracking error. The framework consists of three different approaches: (1) a classic method based on dynamically weighted, interpretable reliability metrics; (2) a lightweight convolutional neural network (CNN) that predicts uncertainty directly from multi-view image data; and (3) a hybrid CNN that adaptively optimizes metric weights while preserving interpretability. The uncertainty level is defined on a fixed scale, with 0%0% corresponding to ideal tracking (error of 0 mm) and 100%100% to a tracking error of 10 mm.\n\nResults:: Experimental validation on clinical and clinically realistic laboratory datasets of ∼ ∼30,000 frames demonstrates a strong positive correlation between uncertainty and error (Pearson r > 0.82r>0.82) and achieves a stable tracking error estimation (error difference <0.6<0.6 mm) with real-time performance (5 ms per frame).\n\nConclusion:: By enabling an intuitive uncertainty-to-error mapping, the proposed framework supports more informed intra-procedural decision-making, enhances operator trust in guidance data, and establishes a practical basis for integration into uncertainty-aware CT-guided intervention systems.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11548-026-03725-w.",
  "abstract_shown": true,
  "affiliations": [
    "Otto von Guericke University Magdeburg, Faculty of Computer Science, Chair of Virtual and Augmented Reality, Universitaetsplatz 2, 39106 Magdeburg, Germany",
    "Research Campus STIMULATE, Otto-Hahn-Str. 2, 39106 Magdeburg, Germany"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Max Steiger",
      "orcid": "0000-0002-0979-0905"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Mohammad Rezapourian",
      "orcid": "0000-0002-1810-6511"
    },
    {
      "affs": [
        0
      ],
      "name": "Marko Rak"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Christian Hansen",
      "orcid": "0000-0002-5734-7529"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dqdr91mtewpe0.bib",
  "cited_by": 0,
  "doi": "10.1007/s11548-026-03725-w",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13723632/",
  "id": "dqdr91mtewpe0",
  "issns": [
    "1861-6410",
    "1861-6429"
  ],
  "landing_url": "https://doi.org/10.1007/s11548-026-03725-w",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7163713441",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s11548-026-03725-w.pdf",
  "pmcid": "PMC13723632",
  "pmid": "42247021",
  "published": "2026-06-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dqdr91mtewpe0.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:22:14Z",
      "native_id": "PMC13723632",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s11548-026-03725-w"
    }
  ],
  "subjects": [],
  "title": "Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments",
  "title_text": "Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:25Z",
  "url": "https://research.draconitic.com/paper/dqdr91mtewpe0",
  "venue": "International Journal of Computer Assisted Radiology and Surgery",
  "versions": []
}