Browsing by Author "Pichler, BJ"

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    A data driven method for estimation of Bavail and appKD using a single injection protocol with [11C]raclopride in the mouse
    (Elsevier Inc., 2014-10-01) Wimberley, CA; Fischer, K; Reilhac, A; Pichler, BJ; Grégoire, MC
    Purpose The partial saturation approach (PSA) is a simple, single injection experimental protocol that will estimate both Bavail and appKD without the use of blood sampling. This makes it ideal for use in longitudinal studies of neurodegenerative diseases in the rodent. The aim of this study was to increase the range and applicability of the PSA by developing a data driven strategy for determining reliable regional estimates of receptor density (Bavail) and in vivo affinity (1/appKD), and validate the strategy using a simulation model. Methods The data driven method uses a time window guided by the dynamic equilibrium state of the system as opposed to using a static time window. To test the method, simulations of partial saturation experiments were generated and validated against experimental data. The experimental conditions simulated included a range of receptor occupancy levels and three different Bavail and appKD values to mimic diseases states. Also the effect of using a reference region and typical PET noise on the stability and accuracy of the estimates was investigated. Results The investigations showed that the parameter estimates in a simulated healthy mouse, using the data driven method were within 10±30% of the simulated input for the range of occupancy levels simulated. Throughout all experimental conditions simulated, the accuracy and robustness of the estimates using the data driven method were much improved upon the typical method of using a static time window, especially at low receptor occupancy levels. Introducing a reference region caused a bias of approximately 10% over the range of occupancy levels. Conclusions Based on extensive simulated experimental conditions, it was shown the data driven method provides accurate and precise estimates of Bavail and appKD for a broader range of conditions compared to the original method. © 2014 Elsevier Inc.
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    Optimisation of PET data processing for a single injection experiment with [11C]Raclopride using a simulations based approach
    (Society of Nuclear Medicine, 2014-11-05) Wimberley, CA; Angelis, GI; Boisson, F; Callaghan, PD; Fischer, K; Pichler, BJ; Meikle, SR; Grégoire, MC; Reilhac, A
    Objectives Positron 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) (a simple, single injection experiment, Delforge 1995) allow the estimation of receptor density (Bavail) and the in vivo affinity 1/(KD). To achieve accurate and stable parameter estimates, and the ability to detect small changes in these parameters, the impact of the data processing chain should be investigated and optimised. Methods Two groups of PET scans were generated for a Partial Saturation Approach (PSA) experiment using Monte Carlo simulation software with a biological phenomenon inferred between the groups. The kinetic parameters Bavail and KD were estimated and the impact of spatial smoothing, temporal denoising and image resolution recovery on the statistical detectability of change in the estimates was investigated. Results Before optimisation, the inferred Bavail difference between the two groups was underestimated by 42% and detected in 66% of cases (at p<0.05), while a false decrease of KD by 13% was detected in more than 11% of cases. After optimisation, the calculated Bavail difference was underestimated by only 3.7% and detected in 89% of cases, while a false slight increase of KD by 3.7 % was detected in only 2% of cases. Conclusions The use of Monte Carlo generated PET scans allowed the optimisation of the data processing chain in order to reliably estimate and detect changes in the parameters Bavail and KD.
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    Simulation-based optimisation of the PET data processing for partial saturation approach protocols
    (Elsevier B.V., 2014-08-15) Wimberley, CA; Angelis, GI; Boisson, F; Callaghan, PD; Fischer, K; Pichler, BJ; Meikle, SR; Grégoire, MC; Reilhac, A
    Positron 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..