{
  "abstract": "ObjectivesTo evaluate the impact of an accelerated whole-body diffusion-weighted MRI (WB-DWI) protocol using deep learning (DL) reconstruction on image quality and apparent diffusion coefficient (ADC) quantification in patients with metastatic prostate cancer.\n\nMethodsThis single-centre prospective study involved two patient cohorts undergoing standard-of-care WB-MRI at 1.5T. The DL WB-DWI acquisition used fewer signal averages and higher parallel imaging acceleration, reconstructed with a research application DL package based on a variational network. In Cohort 1 (n = 10), qualitative assessment of image quality was performed across four anatomical stations using a 4-point Likert scale by two radiologists, blinded to the DWI protocol. In Cohort 2 (n = 20), ADC values were evaluated in hypercellular focal bone metastases (as determined by a radiologist) and compared between the standard and accelerated DL WB-DWI protocols. Image quality scores were analysed using Wilcoxon signed-rank tests (Bonferroni-corrected), and ADC agreement was evaluated via Bland-Altman analysis.\n\nResultsAccelerated DL WB-DWI reduced WB-DWI acquisition time by 37% without compromising image quality. Radiologists consistently rated both protocols as good-to-excellent across all image quality metrics. Median ADC estimates in lesions showed no significant difference between protocols; interquartile range of ADC estimates also showed no significant difference.\n\nConclusionsAccelerated DL WB-DWI enables 37% scan time reduction without degrading image quality or affecting ADC estimates of lesions in patients with metastatic prostate cancer.\n\nAdvances in knowledgeDL-based reconstruction enables faster WB-DWI acquisition without compromising image quality or ADC quantification, supporting clinical implementation in metastatic prostate cancer staging and therapy assessment.",
  "abstract_shown": true,
  "affiliations": [
    "MRI Unit, The Royal Marsden NHS Foundation Trust, London, SM2 5PT, UK, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, SW3 6JB,"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "E. Kousi"
    },
    {
      "name": "M. Rata"
    },
    {
      "name": "D.-M. Koh"
    },
    {
      "name": "C. Messiou"
    },
    {
      "name": "N. Tunariu"
    },
    {
      "name": "G. Hopkinson"
    },
    {
      "name": "E. Evans"
    },
    {
      "name": "O. Darwish"
    },
    {
      "name": "E. Weiland"
    },
    {
      "name": "J. Winfield"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0cpbvteqxvzh8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.17.26362983",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.17.26362983v1.full",
  "id": "0cpbvteqxvzh8",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.17.26362983v1",
  "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": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7213937692",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.17.26362983v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0cpbvteqxvzh8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:02Z",
      "native_id": "10.64898/2026.09.17.26362983",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.17.26362983v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Evaluation of accelerated whole-body diffusion weighted imaging with deep learning reconstruction in patients with metastatic prostate cancer: assessment of image quality and ADC estimates.",
  "title_text": "Evaluation of accelerated whole-body diffusion weighted imaging with deep learning reconstruction in patients with metastatic prostate cancer: assessment of image quality and ADC estimates.",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:02Z",
  "url": "https://research.draconitic.com/paper/0cpbvteqxvzh8",
  "venue": "medRxiv",
  "versions": []
}