{
  "abstract": "Background: This study evaluated interreader agreement and longitudinal performance of MRI methods for knee cartilage volume, thickness, and defect-area quantification. Methods: AI-presegmented masks from 1,189 phase III examinations underwent independent correction by two readers and adjudication. Cartilage volume, three-dimensional ray-tracing thickness (3D-RT), and ray-based defect area (3D-RBA), defined by a 1.5-mm thickness threshold, were calculated. Agreement was assessed using segmentation metrics, intraclass correlation coefficients (ICCs), repeated-measures Bland-Altman analysis, and minimal detectable change at 95% confidence (MDC95). The 3D-RBA framework was evaluated in 120 digital-phantom experiments from 40 participants. Longitudinal analyses included 374 participants, alternative-method comparisons included 65, and retrospective phase II analysis included 24 participants with four visits. Results: Overall AI-to-adjudicated-mask Dice was 0.964 +/- 0.029. Interreader ICCs for volume, thickness, and defect area were 0.956, 0.904, and 0.932; corresponding MDC95 values were 1,596.9 mm^3, 0.227 mm, and 147.4 mm^2. Geometric mean absolute percentage error for defect area was 5.62%, with spatial Dice of 0.961. In 374 participants, volume changes correlated positively with thickness changes (rho=0.431) and negatively with defect-area changes (rho=-0.221). Within-participant phase II correlations followed the same directions in both groups. Conclusions: The workflow demonstrated good interreader agreement. Controlled geometric results and longitudinal associations supported the feasibility of threshold-based defect-area estimation. Volume, thickness, and defect area provide complementary measures of cartilage structure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08081",
  "author_total": 13,
  "authors": [
    {
      "name": "Binbin Yang"
    },
    {
      "name": "Yongmei Jian"
    },
    {
      "name": "Chenglei Liu"
    },
    {
      "name": "Rui Huang"
    },
    {
      "name": "Hongda Shao"
    },
    {
      "name": "Linjun Tong"
    },
    {
      "name": "Yuanjing Xu"
    },
    {
      "name": "Jingshu Wu"
    },
    {
      "name": "Chengzhang He"
    },
    {
      "name": "Suting Peng"
    },
    {
      "name": "Ming Xiao"
    },
    {
      "name": "Yinan Chen"
    },
    {
      "name": "Qi Duan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2kqj96dxacpr3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08081",
  "fulltext_url": null,
  "id": "2kqj96dxacpr3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08081",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7211996491",
  "pdf_url": "https://arxiv.org/pdf/2609.08081",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2kqj96dxacpr3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:59Z",
      "native_id": "2609.08081",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08081"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Reliability assessment and multicenter clinical application of magnetic resonance methods for knee cartilage quantification",
  "title_text": "Reliability assessment and multicenter clinical application of magnetic resonance methods for knee cartilage quantification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:59Z",
  "url": "https://research.draconitic.com/paper/2kqj96dxacpr3",
  "venue": "arXiv",
  "versions": []
}