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UBC Theses and Dissertations

Novel automated approach to the quantitative analysis of dopaminergic functional images in a large cohort of Parkinson's patients Shenkov, Nikolay


PET and SPECT are nuclear medicine imaging techniques that allow for the study of physiological processes in vivo. These techniques allow to assess the dopaminergic system in subjects with Parkinson's disease (PD), which is the system most severely affected by the disease. Parkinson’s Progression Markers Initiative is a multicenter, longitudinal study aimed at identifying novel biomarkers of PD progression. This study utilizes brain SPECT/PET imaging to investigate the dopaminergic system, by examining the distribution of the dopamine transporter (DaT) or the vesicular monoamine transporter 2 (VMAT) in the striatum. Several imaging metrics can be used to quantify the dopaminergic tracer binding in the striatum. These metrics are typically calculated on regions of interest (ROIs) that require either manual placement or coregistration with MR structural images. In the first part of this work, an automated approach to quantifying dopaminergic tracer binding is presented; the method consists of a new metric, SI, evaluated over a bounding box that is automatically placed on the SPECT/PET images. In order to validate this metric, the correlation is computed between the SI values and the motor scores of PD subjects from the PPMI database. We find that sum intensity achieves correlations as strong as the ones obtained using conventional approaches such as the putamen binding ratio, evaluated on manually-placed ROIs, but using a simplified and operator-independent approach. The second part of this work focuses on predicting the rate of PD progression over the four years during which the PD subjects were enrolled in the PPMI study. Two methods of quantifying disease progression are considered. The first approach uses imaging features collected at year-0 of the study to predict the decline in the putamen binding ratios over the next four years. The model achieves a prediction error of 13% for the better side of the putamen, which is comparable to the test-retest reproducibility of this metric. The second approach uses imaging and clinical features at year-0 to predict the clinical outcome (quantified by year-4 motor and cognitive scores). Novel combinations of clinical and imaging features that are predictors of disease severity are identified.

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