Department of Biomedical Sciences

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Browsing Department of Biomedical Sciences by Subject "18F-fallypride"

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    Quantification of positron emission tomography brain imaging using 18F-Fallypride: a simulation
    (Stellenbosch : Stellenbosch University, 2019-04) Mohlapholi, Mohlapoli Stadium; Trauernicht, Christoph; Warwick, James; Du Toit, Monique; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences: Medical Physiology.
    ENGLISH ABSTRACT: 18F-fallypride is of interest in neurological and psychiatric diseases to visualise dopamine D2/D3 receptors in striatal and extrastriatal regions. Several simplified methods of quantifying positron emission tomography (PET) brain imaging using 18F-fallypride have been described and used on real data using non-invasive methods, but a systematic analysis of their effect on quantification has not been performed. In this study, mathematical simulations were used to study this effect on quantification using different models to quantify 18F-fallypride PET in the human brain. The specific uptake areas of interest used in this study for a human brain are the putamen (high receptor density region), thalamus (moderate receptor density region) and temporal cortex (low receptor density region). Materials and methods All simulations were performed using Matlab (version R2013a (8.1.0.604) win 64-bit software; MathWorks, Inc.). Simulations of realistic measurements were performed by modelling varying frame duration, decay of the tracer, and varying noise levels, starting from ideal tissue curves. A modelled input function was used for the generation of ideal tissue curves. Quantification was carried out with arterial blood sampling models: a 2T4k (two-tissue (or three-compartment) reversible model was used and a Logan graphical analysis was applied. Using the cerebellum as the reference region, we studied the following reference region models: Logan graphical analysis (Loganref) using a reference region, the reference tissue model (RTM) and the simplified reference tissue model (SRTM). Finally, the standard uptake value ratio (SUVR) was studied as the simplest method. For the assessment, the results were analysed using a correlation analysis and a Bland-Altman analysis and the relative error (%) and the 5th and 95th percentiles in high/moderate/low receptor density region of the brain were determined. Validation of each method was done in terms of bias and variance. Results The study showed that in all cases the ground truth method and the graphical method using an arterial input function were nearly identical. The overall accuracy and variability of various methods were determined successfully. Loganref was the most accurate, with the highest precision as the replacement of the invasive arterial blood sampling in low/moderate receptor density regions. While SRTM gave high precision in high receptor density region, SUVR calculations produced relatively large errors in all receptor density regions used in this study. The effect of SNR (signal to noise ratio) on quantification was clearly observed since the bias in all reference region methods increased with increasing noise. When the noise level is too high, bias may become too large. Investigating the effect of the models’ performance under a continuous sampling scheme showed the stability in the kinetics of 18F-fallypride using non-invasive reference region methods when the scan length (continuous sampling scheme) was > 150 min and > 120 min in high and in low/moderate receptor density regions respectively. To achieve a good compromise between accuracy and patient comfort, breaks can be introduced at pre-determined intervals. Best results were achieved when the total scan time was 90 min and the total break time was 150 min (i.e. 30 min scan + 30 min break + 30 min scan + 120 min break + 30 min scan). Conclusion Simplified models can be used to provide useful estimates of dopamine transporters that are comparable to methods using arterial blood sampling. However, these models should be used with great care as they can be affected by the noise in the data, the length of the scan duration and the length and position of the breaks in the imaging sequence.