Evaluation of intracerebral ventricles volume of patients with Parkinson's disease using the atlas-based method: A methodological study.
Knowing the volumetric changes in brain can allow for the estimation of the disease progression of various neurodegenerative disorders. Many studies have been shown that the volumetric changes in the some brain structures especially including the dopaminergic neurons, in patients with Parkinson's disease (PD). The objective of this study was to compare intracerebral ventricles volume in patients with PD and healthy subjects to compare an automated atlas-based method (MRIStudio software) and a manual method (ImageJ). T1-weighted brain Magnetic Resonance Imaging (MRI) data of 21 patients with PD and 20 healthy individuals were used to calculate the intracerebral ventricle volumes. Measurement results obtained by ImageJ were considered as the gold standard. We found a significant increase in the left occipital part of the lateral ventricle volume in the patients with PD compared to the control subjects (p < 0.05). Also, no significant difference was found between the two methods of measurement (p > 0.05), meaning that a substantial agreement was found between the results obtained with the atlas-based analysis and manual method. The present study showed that MRIStudio can be performed easily and accurately on routine MRI scans for which the total intracerebral ventricles volume is to be estimated in PD. We suggest that, the attained volume values of intracerebral ventricles may provide a precious data for volumetric dependences of the anatomical structures in several clinical conditions.
['Kocaman|Hikmet|H|', 'Acer|Niyazi|N|', 'Köseoğlu|Emel|E|', 'Gültekin|Murat|M|', 'Dönmez|Halil|H|']
D000368:Aged D001271:Atlases as Topic D002552:Cerebral Ventricles D005260:Female D006801:Humans D007090:Image Interpretation, Computer-Assisted D008279:Magnetic Resonance Imaging D008297:Male D008875:Middle Aged D059906:Neuroimaging D010300:Parkinson Disease
Intracerebral ventricles MRIStudio MRI Atlas-based analysis Brain parcellation Parkinson’s disease