{
  "abstract": "Purpose: To evaluate, within a synthetic pulmonary-artery benchmark, whether biomarker-aware super-resolution improves hemodynamic endpoint fidelity in 4D flow MRI. Methods: A 3D residual channel attention network (RCAN) was trained on 22 CFD simulations from 11 pulmonary-artery geometries with 2x spatial degradation. The original primary comparison evaluated full biomarker-aware training against a combined divergence-temporal-regularization control across 14 matched seeds on a fixed six-case test set. Additional matched analyses subsequently separated divergence and adjacent-frame temporal-difference regularization and evaluated biomarker supervision in a controlled 2x2 design with a common reconstruction schedule. Results: In the original primary comparison, biomarker-aware training reduced PI error relative to the matched combined-regularization control (12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015). In the matched factorial analysis, biomarker supervision reduced PI error without (-2.235 percentage points [95% CI -3.514, -0.849]) and with (-4.125 percentage points [95% CI -5.392, -2.732]) combined regularization. PI improvement was not mirrored by uniformly improved reconstruction metrics: PSNR changed by -0.058 (p = 0.50) and +0.144 dB (p = 0.28), while SSIM decreased by 0.009 (p = 0.004) and 0.007 (p = 0.009), respectively. Divergence alone changed PI by +0.034 percentage points [95% CI -0.705, 0.809] while reducing DivRMS by 1.869 percentage points; temporal-difference regularization increased PI error by 1.562 percentage points [95% CI 0.594, 2.439]. Conclusion: Within this synthetic, fixed-case RCAN benchmark, biomarker supervision improved PI under both regularization conditions. PI improvement was not mirrored by uniformly improved reconstruction metrics. Temporal-difference - not divergence - regularization was the only isolated component associated with reproducible PI deterioration. These findings are configuration-specific and require geometry-independent and in vivo validation.",
  "abstract_shown": true,
  "affiliations": [
    "CIC biomaGUNE, BRTA, San Sebastian, Spain; Universidad del Pais Vasco, Leioa, Spain"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Zubillaga-Unsain"
    },
    {
      "name": "A. Lluveras-Sires"
    },
    {
      "name": "C. Caballero-Gaudes"
    },
    {
      "name": "J. Ruiz-Cabello"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6nys7yvdeyzsd.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.25.754495",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.25.754495v1.full",
  "id": "6nys7yvdeyzsd",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.25.754495v1",
  "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": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7214610276",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.25.754495v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6nys7yvdeyzsd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:50Z",
      "native_id": "10.64898/2026.09.25.754495",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.25.754495v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Biomarker-Aware Super-Resolution for 4D Flow MRI in a Synthetic Pulmonary-Artery Benchmark",
  "title_text": "Biomarker-Aware Super-Resolution for 4D Flow MRI in a Synthetic Pulmonary-Artery Benchmark",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:50Z",
  "url": "https://research.draconitic.com/paper/6nys7yvdeyzsd",
  "venue": "bioRxiv",
  "versions": []
}