{
  "abstract": "Objectives: To prospectively validate an AI-accelerated MRI protocol combining L₁-regularized compressed sensing and deep learning reconstruction for colon cancer staging, compared with contrast-enhanced CT (CECT).\n\nMaterials and methods: This single-center study (August 2022–April 2024) enrolled 132 patients with pathologically confirmed colon cancer who underwent both 3.0-T non-contrast MRI and CECT within 1 week. Patients unable to complete an MRI were excluded. MRI sequences included T1- and T2-weighted imaging and diffusion-weighted imaging. Image quality was assessed using a 4-point Likert scale, alongside quantitative analysis of contrast-to-noise ratio (CNR) and edge rise distance (ERD). Surgical pathology correlation was available for 33 cases. Five radiologists independently reviewed the images.\n\nResults: Among 153 matched examination pairs from 132 patients (mean age, 58 ± 11 years), AI-MRI outperformed CECT in both qualitative and quantitative measures. MRI yielded higher image quality scores (4 [IQR 3–4] vs. 3 [IQR 3–3]), improved CNR (5.5 [IQR 4.2–7.0] vs. 1.3 [IQR 0.7–2.2]), and reduced ERD (1.6 mm [IQR 1.2–2.8] vs. 3.7 mm [IQR 2.8–5.0]). MRI provided superior peritoneal sharpness (75.8%) and colonic wall visualization (100%), and superior or equivalent tumor conspicuity (92.2%) and nodal assessment (86.9%), whereas CECT demonstrated better artifact control (45.1%). In preliminary analyses, MRI achieved higher T-staging accuracy (78.8% vs. 30.3%) and N-staging specificity (93.3% vs. 13.3%).\n\nConclusion: The AI-accelerated MRI protocol significantly outperformed CECT in image quality and preliminary staging accuracy for colon cancer evaluation while eliminating radiation exposure.\n\nKey Points: Question Can an AI-accelerated MRI protocol provide superior image quality and staging performance compared with contrast-enhanced CT, which has limited diagnostic accuracy for colon cancer staging?\n\nFindings AI-accelerated MRI using L₁-regularized compressed sensing and deep learning reconstruction achieved superior image quality and higher preliminary staging accuracy than contrast-enhanced CT.\n\nCritical Relevance Statement AI-accelerated MRI improves image quality and staging confidence without radiation exposure, supporting its potential to enhance preoperative assessment and clinical decision-making in colon cancer.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Zhen Guan"
    },
    {
      "name": "Nan Sun"
    },
    {
      "name": "Tian-Jing Chang"
    },
    {
      "name": "Xiao Zhang"
    },
    {
      "name": "Sha Liu"
    },
    {
      "name": "Zi-Ran Li"
    },
    {
      "name": "Jun Yang"
    },
    {
      "name": "Rui-Jia Sun"
    },
    {
      "name": "Xiao-Yan Zhang"
    },
    {
      "name": "Ying-Shi Sun",
      "orcid": "0000-0001-9424-1910"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9k6pyekw80kx4.bib",
  "cited_by": 0,
  "doi": "10.1186/s13244-026-02445-8",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13731590/",
  "id": "9k6pyekw80kx4",
  "issns": [
    "1869-4101"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13244-026-02445-8",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s13244-026-02445-8.pdf",
  "pmcid": "PMC13731590",
  "pmid": "42975842",
  "published": "2026-11-03",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/9k6pyekw80kx4.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:27:25Z",
      "native_id": "10.1186/s13244-026-02445-8",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13244-026-02445-8"
    },
    {
      "first_seen": "2026-11-08T12:21:45Z",
      "native_id": "PMC13731590",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s13244-026-02445-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1869-4101",
      "subfield": "imaging"
    }
  ],
  "title": "AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT",
  "title_text": "AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:45Z",
  "url": "https://research.draconitic.com/paper/9k6pyekw80kx4",
  "venue": "Insights into Imaging",
  "versions": []
}