{
  "abstract": "Background: Virtual contrast-enhanced (vCE) breast MRI is a deep learning approach for generating contrast-like images from non-contrast acquisitions. As diffusion-weighted imaging (DWI) has emerged as a major contributor to enhancement synthesis, understanding the influence of DWI-related image artifacts is important for the robustness and potential clinical implementation of AI-based contrast-free imaging approaches. We evaluated the robustness of a pix2pix-based vCE synthesis model to artifact-contaminated DWI during training and inference.\n\nMaterials and methods: This IRB-approved, retrospective study included 1,974 multiparametric breast MRI examinations (mean age: 49 years) acquired with two clinical routine 3T MRI scanners (2017–2022). High b-value (b = 1500 s/mm2) DWI images were automatically classified for high-intensity (HI) and low-intensity (LI) artifacts using a pretrained neural network. A pix2pix 2D-GAN was trained either after excluding artifact-affected slices (vCE-clean) or using all available slices (vCE-all). Performance was evaluated using image similarity metrics, lesion-based analysis, reader assessment, and correlation of artifact scores with reconstruction errors.\n\nResults: Excluding artifact-affected slices during training resulted in a small but statistically significant increase in whole-image structural similarity index (SSIM) with small effect size (vCE-all 0.843 ± 0.034; vCE-clean 0.849 ± 0.031, p < 0.001, dav = 0.20). However, lesion-based metrics and qualitative ratings of lesion enhancement and image quality were comparable between models. During Inference, strong LI DWI artifacts were associated with higher lesion-based errors.\n\nConclusion: Controlling for artifacts on diffusion weighted imaging (DWI) during vCE training only minorly influenced technical metrics but not perceived image quality/lesion enhancement. In contrast, severe signal-loss artifacts during inference impaired local performance, highlighting the importance of image quality control during potential deployment.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12880-026-02996-8.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany",
    "Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Germany",
    "Institute of Computer Science, Polish Academy of Science, Warsaw, Poland",
    "Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany",
    "Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany",
    "Diagnostische Radiologie, Radiologie München, München, Germany"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Luise Brock"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Ameya Markale"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Tri-Thien Nguyen"
    },
    {
      "affs": [
        0
      ],
      "name": "Marie Metz"
    },
    {
      "affs": [
        0
      ],
      "name": "Aju George"
    },
    {
      "affs": [
        0
      ],
      "name": "Shirin Heidarikahkesh"
    },
    {
      "affs": [
        0
      ],
      "name": "Hannes Schreiter"
    },
    {
      "affs": [
        0
      ],
      "name": "Julian Pfann Hossbach"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Lorenz A. Kapsner"
    },
    {
      "affs": [
        0
      ],
      "name": "Sabine Ohlmeyer"
    },
    {
      "affs": [
        5
      ],
      "name": "Evelyn Wenkel"
    },
    {
      "affs": [
        0
      ],
      "name": "Michael Uder"
    },
    {
      "affs": [
        0
      ],
      "name": "Frederik B. Laun"
    },
    {
      "affs": [
        1
      ],
      "name": "Florian Knoll"
    },
    {
      "affs": [
        0
      ],
      "name": "Sebastian Bickelhaupt"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8r583ke06c66n.bib",
  "cited_by": 0,
  "doi": "10.1186/s12880-026-02996-8",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13729665/",
  "id": "8r583ke06c66n",
  "issns": [
    "1471-2342"
  ],
  "landing_url": "https://doi.org/10.1186/s12880-026-02996-8",
  "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",
      "Research"
    ],
    [
      "openalex",
      "2741|MRI in cancer diagnosis"
    ]
  ],
  "openalex": "W7225147510",
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s12880-026-02996-8.pdf",
  "pmcid": "PMC13729665",
  "pmid": "42977908",
  "published": "2026-11-03",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/8r583ke06c66n.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:22:22Z",
      "native_id": "PMC13729665",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12880-026-02996-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|MRI in cancer diagnosis",
      "subfield": "imaging"
    }
  ],
  "title": "Influence of dominant sequence artifacts on generative AI for virtual contrast-enhancement in breast MRI",
  "title_text": "Influence of dominant sequence artifacts on generative AI for virtual contrast-enhancement in breast MRI",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:22:22Z",
  "url": "https://research.draconitic.com/paper/8r583ke06c66n",
  "venue": "BMC Medical Imaging",
  "versions": []
}