@article{oai:repo.qst.go.jp:00049081, author = {Martin, Nørgaard and Ganz, Melanie and Svarer, Claus and Feng, Ling and Ichise, Masanori and Lanzenberger, Rupert and Lubberink, Mark and V Parsey, Ramin and Politis, Marios and A Rabiner, Eugenii and Slifstein, Mark and Sossi, Vesna and Suhara, Tetsuya and S Talbot, Peter and Turkheimer, Federico and C Strother, Stephen and M Knudsen, Gitte and Ichise, Masanori and Suhara, Tetsuya}, issue = {2}, journal = {Journal of Cerebral Blood Flow & Metabolism}, month = {Apr}, note = {Positron Emission Tomography (PET) imaging has become a prominent tool to capture the spatiotemporal distribution of neurotransmitters and receptors in the brain. The outcome of a PET study can, however, potentially be obscured by suboptimal and/or inconsistent choices made in complex processing pipelines required to reach a quantitative estimate of radioligand binding. Variations in subject selection, experimental design, data acquisition, preprocessing, and statistical analysis may lead to different outcomes and neurobiological interpretations. We here review the approaches used in 105 original research articles published by 21 different PET centres, using the tracer [11C]DASB for quantification of cerebral serotonin transporter binding, as an exemplary case. We highlight and quantify the impact of the remarkable variety of ways in which researchers are currently conducting their studies, while implicitly expecting generalizable results across research groups. Our review provides evidence that the foundation for a given choice of a preprocessing pipeline seems to be an overlooked aspect in modern PET neuroscience. Furthermore, we believe that a thorough testing of pipeline performance is necessary to produce reproducible research outcomes, avoiding biased results and allowing for better understanding of human brain function}, pages = {210--222}, title = {Cerebral serotonin transporter measurements with [11C]DASB: A review on acquisition and preprocessing across 21 PET centres.}, volume = {39}, year = {2018} }