Simulation-based optimisation of the PET data processing for partial saturation approach protocols

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dc.contributor.authorWimberley, CAen_AU
dc.contributor.authorAngelis, GIen_AU
dc.contributor.authorBoisson, Fen_AU
dc.contributor.authorCallaghan, PDen_AU
dc.contributor.authorFischer, Ken_AU
dc.contributor.authorPichler, BJen_AU
dc.contributor.authorMeikle, SRen_AU
dc.contributor.authorGrégoire, MCen_AU
dc.contributor.authorReilhac, Aen_AU
dc.date.accessioned2018-09-21T00:53:03Zen_AU
dc.date.available2018-09-21T00:53:03Zen_AU
dc.date.issued2014-08-15en_AU
dc.date.statistics2018-09-20en_AU
dc.description.abstractPositron emission tomography (PET) with [11C]Raclopride is an important tool for studying dopamine D2 receptor expression in vivo. [11C]Raclopride PET binding experiments conducted using the Partial Saturation Approach (PSA) allow the estimation of receptor density (Bavail) and the in vivo affinity appKD. The PSA is a simple, single injection, single scan experimental protocol that does not require blood sampling, making it ideal for use in longitudinal studies. In this work, we generated a complete Monte Carlo simulated PET study involving two groups of scans, in between which a biological phenomenon was inferred (a 30% decrease of Bavail), and used it in order to design an optimal data processing chain for the parameter estimation from PSA data. The impact of spatial smoothing, noise removal and image resolution recovery technique on the statistical detection was investigated in depth. We found that image resolution recovery using iterative deconvolution of the image with the system point spread function associated with temporal data denoising greatly improves the accuracy and the statistical reliability of detecting the imposed phenomenon. Before optimisation, the inferred Bavail variation between the two groups was underestimated by 42% and detected in 66% of cases, while a false decrease of appKD by 13% was detected in more than 11% of cases. After optimisation, the calculated Bavail variation was underestimated by only 3.7% and detected in 89% of cases, while a false slight increase of appKD by 3.7% was detected in only 2% of cases. We found during this investigation that it was essential to adjust a factor that accounts for difference in magnitude between the non-displaceable ligand concentrations measured in the target and in the reference regions, for different data processing pathways as this ratio was affected by different image resolutions. © 2014 Elsevier B.V..en_AU
dc.identifier.citationWimberley, C., Angelis, G., Boisson, F., Callaghan, P., Fischer, K., Pichler, B. J.,Meikle, S. R, Grégoire, M. C. & Reilhac, A. (2014). Simulation-based optimisation of the PET data processing for partial saturation approach protocols. NeuroImage, 97, 29-40. doi:10.1016/j.neuroimage.2014.04.010en_AU
dc.identifier.govdoc8902en_AU
dc.identifier.issn1053-8119en_AU
dc.identifier.journaltitleNeuroImageen_AU
dc.identifier.pagination29-40en_AU
dc.identifier.urihttps://doi.org/10.1016/j.neuroimage.2014.04.010en_AU
dc.identifier.urihttp://apo.ansto.gov.au/dspace/handle/10238/9030en_AU
dc.identifier.volume97en_AU
dc.language.isoenen_AU
dc.publisherElsevier B.V.en_AU
dc.subjectPositron computed tomographyen_AU
dc.subjectKineticsen_AU
dc.subjectSimulationen_AU
dc.subjectMonte Carlo Methoden_AU
dc.subjectData processingen_AU
dc.subjectSaturationen_AU
dc.subjectDopamineen_AU
dc.subjectReceptorsen_AU
dc.titleSimulation-based optimisation of the PET data processing for partial saturation approach protocolsen_AU
dc.typeJournal Articleen_AU
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