Advanced Imaging of Parkinson's: Evaluating the Striatum and Substantia Nigra with Datscan Spect and T2W MRI
Year: 2025
Type: conference-paper
Abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the degeneration of dopaminergic neurons in the pars compacta of the substantia nigra, whose projections reach the striatum. Single Photon Emission Computed Tomography (SPECT) and Magnetic Resonance Imaging (MRI) are widely used for the evaluation of PD, providing complementary information on molecular and morphological alterations, respectively.This study aimed to compare segmentation methods applied to DaTSCAN SPECT and T2-weighted MRI (T2W MRI) images in differentiating healthy controls (HC) from PD patients, and to investigate the relationship between striatal dopaminergic loss and structural alterations in the substantia nigra.A total of 173 volumetric brain images were analyzed, including 91 DaTSCAN SPECT (49 HC and 42 from PD patients) and 82 T2W MRI (40 HC and 42 PD). Data were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data).DaTSCAN SPECT images were segmented using four methods: Manual, ThreeBox, Threshold, and Atlas. For T2W MRI images, the intensity and volume of the substantia nigra (SN), as well as the integrity and presence of nigrosome-1, were evaluated. The Binding Potential Index (BPI) was calculated for all segmentation methods applied to SPECT images. The correlation between dopaminergic loss in the striatum and SN was determined using Spearman’s correlation coefficient.The ThreeBox method showed the best performance in distinguishing between HC and PD, while the Threshold method had the lowest discrimination power. BPI analysis revealed a progressive and linear trend of dopaminergic loss in the striatum. For T2W MRI segmentations, the separation between HC and PD based on SN intensity and volume was weak. Correlations between SN intensity and volume with BPI were weak and negative, indicating no significant association between these variables.
Authors: Mesquita, Luís, Ribeiro, Maria Margarida, Vieira, Lina
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6949291533
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