{
  "abstract": "Background: Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) captures 3D anatomy and blood velocity across the cardiac cycle, but current post-processing software is closed, varies across platforms, and sits outside open-source image-computing frameworks. We developed an open-source framework for the import, visualization, and quantification of 4D flow and other high-dimensional CMR within SlicerHeart, an extension for 3D Slicer. Methods: Import 4D Flow reads vendor DICOM, applies preprocessing, and stores velocities as a time-resolved vector field; Analyze 4D Flow provides measurement planes, streamline and Doppler-like visualization, segmentation propagation, and quantification. Underlying both, we extended the Insight Toolkit to store five- and higher-dimensional arrays. Flow quantification was compared with a commercial package (cvi42) and 2D phase contrast in 13 patients with congenital heart disease across 108 paired regions of interest (Bland-Altman analysis and intraclass correlation coefficients, ICCs). Consensus-recommended processing methods were also implemented as an open-source library with an automated test suite of analytic and CFD phantoms and clinical acquisitions. Results: The modules integrate with 3D Slicer's segmentation, registration, and rendering tools and export velocity fields and geometry for computational modeling. Agreement with cvi42 was close: forward volume bias -0.1 mL (95% limits of agreement 2.2 to -2.3; ICC 0.997), effective volume per minute bias -0.015 L/min (0.21 to -0.24; ICC 0.994). Against 2D phase contrast, forward volume bias (-2.2 mL) and mean absolute percentage error (21.8%) matched cvi42 (-2.1 mL; 21.3%). Conclusions: This open, extensible framework brings 4D flow CMR into an established image-computing ecosystem and agrees closely with a commercial reference; every step can be inspected, reproduced, and improved.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": "2610.38948",
  "author_total": 16,
  "authors": [
    {
      "name": "Andras Lasso"
    },
    {
      "name": "Julia Iacovell"
    },
    {
      "name": "Csaba Pinter"
    },
    {
      "name": "Kevin K. Whitehead"
    },
    {
      "name": "Sehdev Pressley"
    },
    {
      "name": "Danish Vaiyani"
    },
    {
      "name": "Matthew Daemer"
    },
    {
      "name": "Katherine Hurm"
    },
    {
      "name": "Analise M. Sulentic"
    },
    {
      "name": "Christian Herz"
    },
    {
      "name": "Ryan T. Black"
    },
    {
      "name": "Seda Aslan"
    },
    {
      "name": "Christopher W. Roy"
    },
    {
      "name": "Mark A. Fogel"
    },
    {
      "name": "Jing Huang"
    },
    {
      "name": "Matthew A. Jolley"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/15w8pe9k1b27b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.38948",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734148/",
  "id": "15w8pe9k1b27b",
  "issns": [
    "2331-8422"
  ],
  "landing_url": "https://arxiv.org/abs/2610.38948",
  "language": "en",
  "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",
      "q-bio.QM"
    ],
    [
      "heading",
      "Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.38948",
  "pmcid": "PMC13734148",
  "pmid": "42990618",
  "published": "2026-10-28",
  "publisher": "arXiv",
  "ris": "https://research.draconitic.com/paper/15w8pe9k1b27b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:36Z",
      "native_id": "2610.38948",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.38948"
    },
    {
      "first_seen": "2026-11-08T12:23:47Z",
      "native_id": "PMC13734148",
      "source": "pmc",
      "url": null
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Open-Source Framework for the Visualization, Quantification, and Analysis of 4D Flow and High-Dimensional Magnetic Resonance Images",
  "title_text": "Open-Source Framework for the Visualization, Quantification, and Analysis of 4D Flow and High-Dimensional Magnetic Resonance Images",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:47Z",
  "url": "https://research.draconitic.com/paper/15w8pe9k1b27b",
  "venue": "arXiv",
  "versions": []
}