An overview of quantitative magnetic resonance imaging analysis studies in the assessment of alzheimer’s disease

Leandrou, S., Petroudi, S., Kyriacou, P. A., Reyes-Aldasoro, C. C. & Pattichis, C. S. (2015). An overview of quantitative magnetic resonance imaging analysis studies in the assessment of alzheimer’s disease. IFMBE Proceedings, 57, pp. 281-286. doi: 10.1007/978-3-319-32703-7_56

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Abstract

Medical image analysis and visualization, can contribute in quantitative and qualitative analysis of Magnetic Resonance Imaging (MRI) towards an earlier diagnosis of Alzheimer’s disease (AD). Moreover, the early detection of Mild Cognitive Impairment (MCI) has recently attracted a lot of attention. The main objective of this paper is to present a survey of recent key papers focused on the classification of MCI and AD and the prediction of conversion from MCI to AD using volume, shape and texture analysis. The most frequent anatomical features used in the assessment of AD, is the hippocampus, the cortex and the local concentration of grey matter. Shape analysis can identify the signs of early hippocampal atrophy, whereas volume analysis evaluates the structure as a whole. Shape analysis seems to be a more accurate technique both in classification of patients and in prognostic prediction. Compared to volume, shape and voxel based morphometry (VBM) techniques, texture analysis can be used to identify the microstructural changes before the larger-scale morphological characteristics which are detected by the other aforementioned techniques. We concluded that quantitative MRI measurements can be used as an in vivo surrogate for the classification of patients and furthermore, for the tracking the Alzheimer’s disease progression.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-32703-7_56
Uncontrolled Keywords: Alzheimer’s disease; Mild Cognitive Impairment; quantitative MRI; temporal lobe; hippocampus; brain volume; prediction; classification
Subjects: R Medicine > RC Internal medicine
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: School of Engineering & Mathematical Sciences > Engineering
URI: http://openaccess.city.ac.uk/id/eprint/14805

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