Guidelines for the content and format of PET brain data in publications and archives: A consensus paper

Gitte M. Knudsen, Melanie Ganz, Stefan Appelhoff, Ronald Boellaard, Guy Bormans, Richard E. Carson, Ciprian Catana, Doris Doudet, Antony D. Gee, Douglas N. Greve, Roger N. Gunn, Christer Halldin, Peter Herscovitch, Henry Huang, Sune H. Keller, Adriaan A. Lammertsma, Rupert Lanzenberger, Jeih San Liow, Talakad G. Lohith, Mark LubberinkChul H. Lyoo, J. John Mann, Granville J. Matheson, Thomas E. Nichols, Martin Nørgaard, Todd Ogden, Ramin Parsey, Victor W. Pike, Julie Price, Gaia Rizzo, Pedro Rosa-Neto, Martin Schain, Peter J.H. Scott, Graham Searle, Mark Slifstein, Tetsuya Suhara, Peter S. Talbot, Adam Thomas, Mattia Veronese, Dean F. Wong, Maqsood Yaqub, Francesca Zanderigo, Sami Zoghbi, Robert B. Innis

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.

Original languageEnglish
Pages (from-to)1576-1585
Number of pages10
JournalJournal of Cerebral Blood Flow and Metabolism
Issue number8
StatePublished - Aug 1 2020


  • Consensus guidelines
  • data sharing
  • data structure
  • open source
  • positron emission tomography


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