PET-MRI analysis to identify metabolic changes during Parkinson's disease

Parkinson disease (PD) is a common neurodegenerative pathology, whose accurate diagnosis is still a challenge. PET imaging obtained with [18F]-fluorodeoxyglucose provides a metabolic pattern, highlighting the brain substructures related to PD, thus constituting a valuable diagnosis tool. Besides, it has been reported that incorporating MRI into the analysis enhances the performance of methods aiming to discriminate between healthy subjects and PD patients. In this research, a methodology is proposed that allows: to integrate structural and metabolic imaging information at specific substructures of interest; to spatially align both modalities; to normalize functional images and to extract the adequate biomarkers. Among structural parameters, compacity and tortuosity are proposed, while metabolic biomarkers are extracted from histogram analyses. The random forest algorithm is used for classification and feature selection tasks. The studied populations consisted of nine patients with PD diagnosis and 12 healthy controls. Structural biomarkers showed a small contribution to discriminate between groups, while metabolic biomarkers resulted in 85% (training) to 100% (final test) accuracies. The proposed methodology is promising to diagnose PD and can be extended to other movement disorders.