{
  "abstract": "BackgroundAutomated brain volumetry is increasingly used for clinical and research assessment, but its outputs depend on acquisition and reconstruction settings. We evaluated how parallel-imaging acceleration and vendor deep-learning reconstruction (Siemens Deep Resolve) affect automated volumetry on T2-weighted turbo spin-echo MRI, and whether deep-learning reconstruction stabilises measurements across protocols.\n\nMethodsFour healthy volunteers each underwent 14 T2-TSE-TRA acquisitions on a 1.5 T scanner, covering seven protocols (baseline, GRAPPA R = 2, 3, 4, and SMS x2, x3, x4), with each protocol acquired separately once with Deep Resolve off and once with Deep Resolve on, for 56 acquisitions in total. Six pipelines were applied: SynthSeg, OpenMAP-T2, and GOUHFI 2.0 for subcortical parcellation, and TotalSegmentator MRI, HD-BET, and NV-Segment-CTMR for whole-brain masking. A three-rater STAPLE consensus of six subcortical structures on the baseline acquisition served as the expert reference. Reference-relative accuracy (absolute percentage error), cross-protocol dispersion (coefficient of variation), and spatial reproducibility (Dice coefficient and 95th-percentile surface distance) were compared between reconstruction states.\n\nResultsDeep Resolve improved reference-relative accuracy most for GOUHFI 2.0 (pooled median absolute percentage error 28.4% to 21.9%), modestly for SynthSeg (16.2% to 15.5%), and had a mixed, structure-dependent effect for OpenMAP-T2. It approximately halved cross-protocol dispersion for all three parcellation tools (coefficient of variation 2.62% to 1.39% for SynthSeg, 4.42% to 2.14% for OpenMAP-T2, and 21.75% to 7.87% for GOUHFI) and improved spatial reproducibility in parallel. It eliminated the systematic GOUHFI under-segmentation seen at high acceleration under conventional reconstruction (median deviation -31.6% at GRAPPA R = 4; 14 failure events under conventional reconstruction, none with Deep Resolve). Whole-brain masking was robust in both states.\n\nConclusionsDeep Resolve improved the robustness of automated brain segmentation in accelerated T2-weighted MRI, reducing cross-protocol variability and improving spatial reproducibility, with the largest reduction in reference-relative error in the most acceleration-sensitive pipeline. The elimination of severe segmentation failures observed under conventional reconstruction highlights its potential to support reliable volumetry at higher acceleration. Reconstruction optimization therefore offers a practical route to more consistent quantitative measurements across acquisition protocols.",
  "abstract_shown": true,
  "affiliations": [
    "AGH University of Krakow, 30-059 Krakow, Poland"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. LASEK"
    },
    {
      "name": "R. Obuchowicz"
    },
    {
      "name": "M. Strzelecki"
    },
    {
      "name": "I. Dwojak"
    },
    {
      "name": "W. Kanas"
    },
    {
      "name": "I. Obuchowicz"
    },
    {
      "name": "A. Krzyzak"
    },
    {
      "name": "J. Minkowski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3fcgcsayn7nwg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.26362537",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362537v1.full",
  "id": "3fcgcsayn7nwg",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362537v1",
  "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": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7211940549",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362537v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3fcgcsayn7nwg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:18Z",
      "native_id": "10.64898/2026.09.08.26362537",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362537v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Automated Brain Segmentation in Accelerated T2-Weighted MRI: Effects of Deep Learning-Based Reconstruction",
  "title_text": "Automated Brain Segmentation in Accelerated T2-Weighted MRI: Effects of Deep Learning-Based Reconstruction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:18Z",
  "url": "https://research.draconitic.com/paper/3fcgcsayn7nwg",
  "venue": "medRxiv",
  "versions": []
}