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Neuroimage. 2014 Aug 15;97:29-40. doi: 10.1016/j.neuroimage.2014.04.010. Epub 2014 Apr 14.

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

Author information

1
Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW, Australia; Brain & Mind Research Institute, University of Sydney, Sydney, NSW, Australia. Electronic address: caw@ansto.gov.au.
2
Brain & Mind Research Institute, University of Sydney, Sydney, NSW, Australia.
3
Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW, Australia; Brain & Mind Research Institute, University of Sydney, Sydney, NSW, Australia.
4
Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard-Karls University of Tübingen, Germany.

Abstract

Positron emission tomography (PET) with [(11)C]Raclopride is an important tool for studying dopamine D2 receptor expression in vivo. [(11)C]Raclopride PET binding experiments conducted using the Partial Saturation Approach (PSA) allow the estimation of receptor density (B(avail)) and the in vivo affinity appK(D). 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 B(avail)), 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 B(avail) variation between the two groups was underestimated by 42% and detected in 66% of cases, while a false decrease of appK(D) by 13% was detected in more than 11% of cases. After optimisation, the calculated B(avail) variation was underestimated by only 3.7% and detected in 89% of cases, while a false slight increase of appK(D) 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.

KEYWORDS:

Data denoising; Data processing method evaluation; Kinetic modelling; Monte Carlo simulation; PET; Partial saturation approach; Partial volume effects

[Indexed for MEDLINE]

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