{
  "abstract": "Volume electron microscopy (VEM) enables nanometer-resolution three-dimensional (3D) visualization of biological specimens via serial sectioning and imaging. Owing to limitations of downstream analysis, VEM datasets are often acquired at slow speeds and high resolutions, thereby limiting achievable imaging throughput. By systematically searching for optimal VEM acquisition conditions, we find that sufficient spatial resolution effectively counteracts high image noise in preserving 3D structural information. To further verify that denoising is more effective in restoring volumetric datasets than axial interpolation, we compared machine learning-based methods, including a newly developed 3D context-based denoising model, through various tasks on VEM datasets acquired simultaneously. Our volumetric approach not only outperforms other baseline methods in faithful feature recovery but also facilitates robust serial block-face cutting down to 20 nm by allowing fast imaging. This work provides both an optimized acquisition strategy and volumetric denoising methods as actionable guidelines for maximizing VEM throughput.",
  "abstract_shown": true,
  "affiliations": [
    "School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Science, Beijing, China",
    "State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China",
    "Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China",
    "School of Future Technology, University of Chinese Academy of Sciences, Beijing, China",
    "School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Bohao Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Fangfang Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Haoyu Wang"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Yanchao Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhuangzhuang Zhao"
    },
    {
      "affs": [
        1,
        4
      ],
      "name": "Haoran Chen"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Hua Han"
    },
    {
      "affs": [
        1
      ],
      "name": "Xi Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Yunfeng Hua"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5d967a8swkawx.bib",
  "cited_by": 1,
  "doi": "10.1016/j.crmeth.2026.101543",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13721863/",
  "id": "5d967a8swkawx",
  "issns": [
    "2667-2375"
  ],
  "landing_url": "https://doi.org/10.1016/j.crmeth.2026.101543",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7172395253",
  "pdf_url": null,
  "pmcid": "PMC13721863",
  "pmid": "42551442",
  "published": "2026-08-04",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/5d967a8swkawx.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:22:28Z",
      "native_id": "PMC13721863",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.crmeth.2026.101543"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1315|Advanced Electron Microscopy Techniques and Applications",
      "subfield": "structural-biology"
    }
  ],
  "title": "Volumetric denoising enables high-throughput volume electron microscopy and efficient downstream analysis",
  "title_text": "Volumetric denoising enables high-throughput volume electron microscopy and efficient downstream analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:28Z",
  "url": "https://research.draconitic.com/paper/5d967a8swkawx",
  "venue": "Cell Reports Methods",
  "versions": []
}