Optimized, Automated Striatal Uptake Analysis Applied to SPECT Brain Scans of Parkinson's Disease Patients

¹ Institute for Neurodegenerative Disorders, and Molecular Neuroimaging, LLC, New Haven, Connecticut

^* To whom correspondence should be addressed. E-mail: gzubal{at}indd.org.

	Abstract

Reliable quantitative dopamine transporter imaging is critical for early and accurate diagnosis of Parkinson's disease (PD). Image quantitation is made difficult by the variability introduced by manual interventions during the quantitative processing steps. A fully automated objective striatal analysis (OSA) program was applied to dopamine transporter images acquired from PD subjects with early symptoms of suspected parkinsonism and compared with manual analysis by a trained image-processing technologist. Methods: A total of 101 ¹²³I--CIT SPECT scans were obtained of subjects recruited to participate in the Query-PD Study. Data were reconstructed and then analyzed according to a package of scripts (OSA) that reorients the SPECT brain volume to the standard geometry of an average scan, automatically locates the striata and occipital structures, locates the caudate and putamen, and calculates the background-subtracted striatal uptake ratio (V3''). The striatal uptake ratio calculated by OSA was compared with manual analysis by a trained image-processing technologist. Several parameters were varied in the automated analysis, including the number of summed transverse slices and the size and separation of the regions of interest applied to the caudate and putamen to determine the optimum OSA analysis. The parameters giving V3'' with the closest correlation to the manual analysis were accepted as optimal. Results: The optimal comparison between the V3'' obtained by the human analyst and that obtained by the automated OSA analysis yielded a correlation coefficient of 0.96. Conclusion: Our optimized OSA delivers V3'' evaluations that closely correlate with a similar evaluation manually applied by a highly trained image-processing technologist.

Key Words: PACS, neurology, SPECT, Parkinson's disease, analysis, automated