{
  "abstract": "Purpose: Variational autoencoders (VAEs) can detect unsupervised anomalies in medical images. We aimed to determine whether a VAE could automatically detect anomalies in myocardial perfusion SPECT (MPS) images. We then interpreted the training and generative processes of the VAE.\n\nMethods: We trained a VAE on polar maps to learn the variability of normal myocardial perfusion profiles in latent space. The training dataset comprised 3,432 polar map images (49% male) without coronary artery disease (CAD) and the validation set contained 111 polar map images (59% male), among whom, (43% had obstructive CAD). This latent representation allowed the VAE to reconstruct synthetic normal images that served as references to detect anomalies. Abnormal myocardial perfusion was automatically identified by measuring differences between generated and original images using cosine similarity. We also summed stress scores (SSS) using sex-segregated normal databases.\n\nResults: The trained VAE generated reference normal polar maps, regardless of normal and defect profiles and sex differences. The areas under the receiver-operating characteristics curve to detect CAD were 0.89 ± 0.041 for cosine similarity and 0.81 ± 0.030 for SSS (p = 0.047). The VAE learned the characteristics of normal male and female myocardial perfusion distributions in the latent space.\n\nConclusion: This VAE-based approach improved CAD detection in MPS images and eliminated the need to determine sex-specific myocardial perfusion characteristics.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00259-026-08161-1.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiation Science, Hirosaki University Graduate School of Health Sciences, 66-1 Hon-cho, Hirosaki-shi, Aomori 036-8564 Japan",
    "Department of Nuclear Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan",
    "Department of Electrical, Electronic and Computer Engineering, Gifu University, Gifu, Japan",
    "Center for One Medicine Innovative Translational Research, Gifu University, Gifu, Japan",
    "Department of Radiological Technology, Kanazawa University Hospital, Kanazawa, Japan"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Koichi Okuda",
      "orcid": "0000-0003-0010-6844"
    },
    {
      "affs": [
        1
      ],
      "name": "Kenichi Nakajima"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Takeshi Hara"
    },
    {
      "affs": [
        4
      ],
      "name": "Hiroto Yoneyama"
    },
    {
      "affs": [
        1
      ],
      "name": "Seigo Kinuya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ckcfjg04s9b74.bib",
  "cited_by": 0,
  "doi": "10.1007/s00259-026-08161-1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13727861/",
  "id": "ckcfjg04s9b74",
  "issns": [
    "1619-7070",
    "1619-7089"
  ],
  "landing_url": "https://doi.org/10.1007/s00259-026-08161-1",
  "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",
      "Original Article"
    ],
    [
      "openalex",
      "2741|Cardiac Imaging and Diagnostics"
    ]
  ],
  "openalex": "W7204641201",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s00259-026-08161-1.pdf",
  "pmcid": "PMC13727861",
  "pmid": "42667309",
  "published": "2026-08-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ckcfjg04s9b74.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:23:39Z",
      "native_id": "PMC13727861",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s00259-026-08161-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Cardiac Imaging and Diagnostics",
      "subfield": "imaging"
    }
  ],
  "title": "Variational autoencoders can detect coronary artery disease in myocardial perfusion SPECT images",
  "title_text": "Variational autoencoders can detect coronary artery disease in myocardial perfusion SPECT images",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:20:58Z",
  "url": "https://research.draconitic.com/paper/ckcfjg04s9b74",
  "venue": "European Journal of Nuclear Medicine and Molecular Imaging",
  "versions": []
}