{
  "abstract": "Ultrathin endoscopy is highly attractive for real-time tissue imaging in narrow and hard-to-reach regions of the body. A single multimode fibre (MMF) is an attractive probe because of its small diameter, flexiblility, and diffraction-limited spatial resolution enabled by the large number of transverse modes guided within a single core. Because the distal fibre tip is inaccessible during endoscopy, reflection-mode imaging, in which the same fibre delivers illumination and collects backscattered light, is more practical than transmission-mode imaging. However, image recovery from the resulting speckle pattern is challenging because light undergoes double-pass propagation through the MMF, with mode coupling and dispersion; the backscattered signal is weak, and the camera records intensity only, without phase information. Here, we propose a single-shot reflection-mode MMF imaging framework that combines a reflected real-valued intensity transmission matrix (reflected-RVITM) with an image restoration network. The reflected-RVITM is calibrated using intensity-only measurements, without interferometry or phase retrieval, and provides a physics-guided initial reconstruction from a single backscattered speckle frame. A restoration network then refines this initial reconstruction instead of inverting the raw speckle. Four restoration backbones are evaluated: HPM-Attention-UNet, GAM, MambaIRv2, and CICPNet. On matched datasets, hybrid models outperformed corresponding networks trained to map raw speckle directly to images. For example, HPM-Attention-UNet on MNIST improved mean PCC from 0.572 to 0.944 (+65.1%). Under domain shift, with training only on Fashion-MNIST and tested on unseen CIFAR scenes, hybrid models achieved mean PCC of 0.61-0.65, compared with 0.36-0.50 for direct learning. This framework is further demonstrated using physical objects at the distal fibre tip. These results demonstrate that a reflected-RVITM physics prior combined with a restoration network enables single-shot image recovery after intensity-only calibration, offering a phase-retrieval-free and generalisable route towards minimally invasive reflection-mode MMF endoscopy.",
  "abstract_shown": true,
  "affiliations": [
    "King's College London"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Z. Ye"
    },
    {
      "name": "F. He"
    },
    {
      "name": "T. Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Xia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3emp2q6206qfs.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.28.26361674",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361674v1.full",
  "id": "3emp2q6206qfs",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361674v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "3102|Random lasers and scattering media"
    ]
  ],
  "openalex": "W7204857457",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361674v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3emp2q6206qfs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.64898/2026.08.28.26361674",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361674v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Image transmission through a multimode fibre in reflection mode with physics-guided deep learning towards ultrathin endoscopy",
  "title_text": "Image transmission through a multimode fibre in reflection mode with physics-guided deep learning towards ultrathin endoscopy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/3emp2q6206qfs",
  "venue": "medRxiv",
  "versions": []
}