Published in:
01-12-2011 | Original article
Automated striatal uptake analysis of 18F-FDOPA PET images applied to Parkinson’s disease patients
Authors:
I-Cheng Chang, Kun-Han Lue, Hung-Jen Hsieh, Shu-Hsin Liu, Chih-Hao K. Kao
Published in:
Annals of Nuclear Medicine
|
Issue 10/2011
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Abstract
Objective
6-[18F]Fluoro-l-DOPA (FDOPA) is a radiopharmaceutical valuable for assessing the presynaptic dopaminergic function when used with positron emission tomography (PET). More specifically, the striatal-to-occipital ratio (SOR) of FDOPA uptake images has been extensively used as a quantitative parameter in these PET studies. Our aim was to develop an easy, automated method capable of performing objective analysis of SOR in FDOPA PET images of Parkinson’s disease (PD) patients.
Methods
Brain images from FDOPA PET studies of 21 patients with PD and 6 healthy subjects were included in our automated striatal analyses. Images of each individual were spatially normalized into an FDOPA template. Subsequently, the image slice with the highest level of basal ganglia activity was chosen among the series of normalized images. Also, the immediate preceding and following slices of the chosen image were then selected. Finally, the summation of these three images was used to quantify and calculate the SOR values. The results obtained by automated analysis were compared with manual analysis by a trained and experienced image processing technologist.
Results
The SOR values obtained from the automated analysis had a good agreement and high correlation with manual analysis. The differences in caudate, putamen, and striatum were −0.023, −0.029, and −0.025, respectively; correlation coefficients 0.961, 0.957, and 0.972, respectively.
Conclusions
We have successfully developed a method for automated striatal uptake analysis of FDOPA PET images. There was no significant difference between the SOR values obtained from this method and using manual analysis. Yet it is an unbiased time-saving and cost-effective program and easy to implement on a personal computer.