Volume estimation of brain ventricles using Cavalieri's principle and Atlas-based methods in Alzheimer disease: Consistency between methods

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Elsevier Sci Ltd

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info:eu-repo/semantics/embargoedAccess

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Automatic estimations of brain ventricles are needed to assess disease progression in neurodegenerative disorders such as Alzheimer Disease (AD). The objectives of this study are to evaluate the diagnostic performances of an automated volumetric assessment tool in estimating lateral ventricle volumes in AD and to compare this with Cavalieri's principle, which is accepted as the gold standard method. This is across-sectional volumetric study including 25 Alzheimer patients and 25 healthy subjects undergoing magnetic resonance images (MRI) with a 3D turbo spin echo sequence at 1.5 Tesla. The Atlas-based method incorporated MRI-Studio software to automatically measure he volumes of brain ventricles. To compare the corresponding measurements, we used manual point-counting and semi-automatic planimetry methods based on Cavalieri's principle. Bland-Altman test results indicated an excellent agreement between Cavalieri's principle and the Atlas-based method in all volumetric measurements (p < 0.05). We obtained a 64% sensitivity and 92% specificity for lateral ventricular volumes according to the Atlas-based method. AD subjects had significantly larger left and right lateral ventricle volume (LVV) when compared to control subjects in respect to three volumetric methods (p < 0.01). Lateral ventricle-to-brain ratio (VBR) statistically increased 49.23% in measurements done with the point-counting method, 45.12% with the planimetry method, and 45.49% with the Atlas-based method in AD patients (p < 0.01). As a result, the Atlas-based method may be used instead of manual volumetry to estimate brain volumes. Additionally, this method provides rapid and accurate estimations of brain ventricular volumes in-vivo examination of MRI.

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Alzheimer Disease, Magnetic Resonance Images, Cavalieri's Principle, Brain Ventricles

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Journal of Clinical Neuroscience

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78

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Onay

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