{
  "abstract": "Sparse-angle X-ray computed tomography (CT) presents a challenge for reliable inference, as limited measurements may leave object boundaries unresolved. Bayesian optimal experimental design (BOED) methods select informative measurements, but can be computationally expensive or require data-driven surrogates and do not explicitly exploit microlocal visibility. We address this limitation by focusing on estimating inclusion boundaries rather than the entire attenuation field. For piecewise-constant objects, these boundaries correspond to singularities characterized by their wavefront sets. Microlocal analysis determines the X-ray view-angles at which these singularities are visible. We combine this information with Bayesian uncertainty quantification to introduce B-MUSE, a microlocal uncertainty-driven sequential experimental design method. We define a microlocal C-optimality criterion associated with the wavefront direction of the most uncertain boundary singularity and prove that its optimal solution satisfies the microlocal visibility condition. B-MUSE uses this result to sequentially select informative view-angles according to boundary uncertainty. It also incorporates an adaptive stopping criterion that terminates acquisition when additional measurements provide no meaningful uncertainty reduction. We evaluate B-MUSE through numerical experiments, including interpretable examples, prior-generated inclusions, and a multi-inclusion example from an external dataset, and benchmark it against recent methods. The results show that B-MUSE substantially reduces posterior uncertainty with few adaptively selected measurements while providing boundary estimates. These findings establish a direct connection between BOED and microlocal visibility and demonstrate the potential of uncertainty-driven acquisition to reduce measurement requirements for goal-oriented X-ray CT without training data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.14528",
  "author_total": 3,
  "authors": [
    {
      "name": "Mansoure Giahi Sabour"
    },
    {
      "name": "Hassan Yazdanian"
    },
    {
      "name": "Babak Maboudi Afkham"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7wp3z601z08bw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.14528",
  "fulltext_url": null,
  "id": "7wp3z601z08bw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.14528",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ],
    [
      "arxiv",
      "math.PR"
    ],
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.14528",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7wp3z601z08bw.ris",
  "sources": [
    {
      "first_seen": "2026-11-13T06:20:56Z",
      "native_id": "2611.14528",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.14528"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math.PR",
      "subfield": "probability"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "A Goal-Oriented Bayesian Experimental Design to Reduce Uncertainty in Sparse-Angle X-ray Computed Tomography",
  "title_text": "A Goal-Oriented Bayesian Experimental Design to Reduce Uncertainty in Sparse-Angle X-ray Computed Tomography",
  "type": "preprint",
  "updated_at": "2026-11-13T06:21:06Z",
  "url": "https://research.draconitic.com/paper/7wp3z601z08bw",
  "venue": "arXiv",
  "versions": []
}