{
  "abstract": "Soft X-ray tomography (SXT) is an emerging modality for whole-cell 3D imaging in near-native states. However, the effective spatial resolution is limited by optical artifacts characterized by the point spread function (PSF). Standard reconstruction methods force a compromise between structural sharpness and noise, failing to fully resolve these depth-dependent artifacts. By embedding experimentally measured, depth-variant PSFs into a differentiable forward model, we demonstrate a physics-aware computational optimization that bypasses these limitations to recover high-frequency cellular ultrastructure. The structural fidelity was validated using split-tilt Fourier ring correlation (FRC), alongside an experimental bead phantom tomogram, providing supporting evidence that the recovered high-frequency features reflect genuine specimen structure rather than fabricated artifacts. Our method effectively increases FRC spatial resolution and recovers cellular ultrastructure. Furthermore, under sparse-angular subsampling, the framework maintained spatial resolution using half the projection angles, a computational proxy pointing toward the potential for reduced radiation exposure in future acquisitions. This hardware-free, computational approach offers a route toward mitigating the optical and dosimetric constraints that currently limit nanoscale soft X-ray tomography.",
  "abstract_shown": true,
  "affiliations": [
    "University College Dublin"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Chueh"
    },
    {
      "name": "E. Gallagher"
    },
    {
      "name": "C. de Ceuninck van Capelle"
    },
    {
      "name": "L. Luo"
    },
    {
      "name": "T. Ishikawa"
    },
    {
      "name": "C. Evans"
    },
    {
      "name": "N. Fletcher"
    },
    {
      "name": "M. Lopez-Perez"
    },
    {
      "name": "D. Rogers"
    },
    {
      "name": "S. O'Connor"
    },
    {
      "name": "C. McIntyre"
    },
    {
      "name": "M. Donnellan"
    },
    {
      "name": "P. Sheridan"
    },
    {
      "name": "J. C. Simpson"
    },
    {
      "name": "S. Kapishnikov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ea6s3153e3rp6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.06.21.730079",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.06.21.730079v3.full",
  "id": "ea6s3153e3rp6",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.06.21.730079v3",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "cell biology"
    ],
    [
      "openalex",
      "3108|Advanced X-ray Imaging Techniques"
    ]
  ],
  "openalex": "W7165640869",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.06.21.730079v3.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ea6s3153e3rp6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:39Z",
      "native_id": "10.64898/2026.06.21.730079",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.06.21.730079v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:cell biology",
      "subfield": "cell-biology"
    }
  ],
  "title": "Physics-aware resolution enhancement of soft X-ray tomography with measurement-supervised learning",
  "title_text": "Physics-aware resolution enhancement of soft X-ray tomography with measurement-supervised learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:39Z",
  "url": "https://research.draconitic.com/paper/ea6s3153e3rp6",
  "venue": "bioRxiv",
  "versions": []
}