{
  "abstract": "Background and purpose: We propose PGPI4MR (prior-guided, physics-informed 4D MRI reconstruction), a deep learning method that leverages information from other respiratory phases or a setup scan, the first 30 s of the same scan, to improve 4D MRI reconstruction without extra scan time. Materials and methods: PGPI4MR is an unrolled network that reconstructs 16 respiratory phases from 60, 30, or 15 s of data, and single 3D volumes, each from 0.29-0.49 s of data, using a setup-scan image. The networks were trained once, on other volunteers, and were not retrained for new volunteers. We evaluated PGPI4MR on measured k-space from three datasets: free-breathing MR-linac (1.5T) and abdominal (3T) scans, and thigh scans with cuff compression (1.5T). All methods, including comparative models, received identical samples and were scored similarly. Results: In vivo, PGPI4MR had the lowest held-out error for 4D MRI at every scan time, and its 16 phases were ready a median of 7.5-13 s after the scan on the 2 GPUs tested. Coupling the 16 respiratory phases reduced the error at 60 s by 9.1% compared with reconstructing each phase alone. For sub-second MRI, the error at 0.49 s was 15.5% lower than that of the best comparison method, classical reconstruction toward the same prior, and on the faster GPU, every 0.29 s window was acquired and reconstructed within 0.5 s. Without retraining, PGPI4MR matched the best methods on the 3T abdominal scans but not on the thigh scans; after retraining on thigh data, it reduced the held-out error by 6.6% relative to the best comparison method. Conclusion: Prior information allowed PGPI4MR to reconstruct 4D and sub-second MRI more accurately than compressed sensing and a published 4D network, within seconds and without extra scan time. This could support 4D MRI for treatment planning and 3D motion monitoring during MR-guided radiotherapy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01888",
  "author_total": 9,
  "authors": [
    {
      "name": "Mojtaba Safari"
    },
    {
      "name": "Parag J. Parikh"
    },
    {
      "name": "Shansong Wang"
    },
    {
      "name": "Mingzhe Hu"
    },
    {
      "name": "Philip Connell"
    },
    {
      "name": "Stanley L. Liauw"
    },
    {
      "name": "Ralph R. Weichselbaum"
    },
    {
      "name": "Zhen Tian"
    },
    {
      "name": "Xiaofeng Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4yeb7rt2gvygz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01888",
  "fulltext_url": null,
  "id": "4yeb7rt2gvygz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01888",
  "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",
      "cs.CV"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01888",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4yeb7rt2gvygz.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.01888",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01888"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Prior-Guided Physics-Informed Deep Learning for Fast 4D MRI Reconstruction",
  "title_text": "Prior-Guided Physics-Informed Deep Learning for Fast 4D MRI Reconstruction",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/4yeb7rt2gvygz",
  "venue": "arXiv",
  "versions": []
}