{Reference Type}: Journal Article
{Title}: Guidelines for the content and format of PET brain data in publications and archives: A consensus paper.
{Author}: Knudsen GM;Ganz M;Appelhoff S;Boellaard R;Bormans G;Carson RE;Catana C;Doudet D;Gee AD;Greve DN;Gunn RN;Halldin C;Herscovitch P;Huang H;Keller SH;Lammertsma AA;Lanzenberger R;Liow JS;Lohith TG;Lubberink M;Lyoo CH;Mann JJ;Matheson GJ;Nichols TE;Nørgaard M;Ogden T;Parsey R;Pike VW;Price J;Rizzo G;Rosa-Neto P;Schain M;Scott PJ;Searle G;Slifstein M;Suhara T;Talbot PS;Thomas A;Veronese M;Wong DF;Yaqub M;Zanderigo F;Zoghbi S;Innis RB;
{Journal}: J Cereb Blood Flow Metab
{Volume}: 40
{Issue}: 8
{Year}: 08 2020
{Factor}: 6.96
{DOI}: 10.1177/0271678X20905433
{Abstract}: 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.