Automated Parkinson's Disease Dopamine Transporter Scan Analysis Fast Track

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DESCRIPTION (provided by applicant): We hypothesize that for a class of dopamine transporter SPECT agents, an automated software processing package can be developed, which will objectively yield striatal quantitative uptake values for evaluating Parkinson's disease (PD) diagnosis and progression. This fully automated and Objective Striatal Analysis (OSA) package will be able to evaluate subjects referred for SPECT neuroreceptor imaging studies with a sensitivity and specificity higher than currently possible by visual inspection or manual image processing methods. OSA will carry out (with no user interaction required) the following processing steps: a) reorientation of the reconstructed brain volume along the cantho-meatal line, b) identification of axial slices with striatal activity and summation of specified number of slices, c) placement of regional template on left and right caudate and putamen and occipital background region following set rules for movement of regions, d) extraction of count density data for determination of regional striatal V3" as indicated in the equation: V3"= (regional striatal count density - occipital count density)/(occipital count density). The required algorithms will be coded into a software package (OSA), and the results of OSA applied to groups of patients and healthy volunteers will be compared to the results obtained by manually analyzing the same subject groups by a highly-trained image processing technologist. We propose to develop a completely automated package for analysis of presynaptic dopaminergic function in Parkinson's patients which can be easily used in a clinical setting for high quality quantitative imaging assessments of neuroreceptor SPECT brain images. This automated analysis will remove subjectivity in the determination of the imaging outcome measure (progression of disease), and permit a clinical imaging center to obtain accurate diagnostic and monitoring assessments of the patient with reference to a normative dataset.