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

Article English OPEN
Leandrou, S. ; Petroudi, S. ; Kyriacou, P. A. ; Reyes-Aldasoro, C. C. ; Pattichis, C. S. (2015)

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.
  • References (53)
    53 references, page 1 of 6

    R. C. Petersen, “Mild cognitive impairment as a diagnostic entity,” J. Intern. Med., vol. 256, no. 3, pp. 183-194, Sep. 2004.

    Gauthier, G. Jicha, K. Meguro, J. O'brien, F. Pasquier, P. Robert, M. Rossor, S. Salloway, Y. Stern, P. J. Visser, and P. Scheltens, [17] “Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria,” Lancet Neurol., vol. 6, no. 8, pp. 734-746, Aug. 2007.

    C. R. Jack, D. S. Knopman, W. J. Jagust, L. M. Shaw, P. S. Aisen, M. W. Weiner, R. C. Petersen, and J. Q. Trojanowski, “Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade,” Lancet Neurol., vol. 9, no. 1, pp. 119-128, Jan.

    Natl. Acad. Sci. U. S. A., vol. 99, no. 7, pp. 4703-4707, Apr. 2002.

    L. R. Squire, C. E. L. Stark, and R. E. Clark, “The medial temporal lobe,” Annu. Rev. Neurosci., vol. 27, pp. 279-306, 2004.

    287, no. 18, pp. 2335-2338, May 2002.

    Weiner, “The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods,” J. Magn. Reson. Imaging, vol. 27, no. 4, pp. 685-691, Apr. 2008.

    Engl., vol. 360, no. 9347, pp. 1759-1766, Nov. 2002.

    O. Colliot, G. Chételat, M. Chupin, B. Desgranges, B. Magnin, H. Benali, B. Dubois, L. Garnero, F. Eustache, and S. Lehéricy, “Discrimination between Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus,” Radiology, vol. 248, no. 1, pp. 194-201, Jul. 2008.

    Lehéricy, H. Benali, L. Garnero, and O. Colliot, “Fully Automatic Hippocampus Segmentation and Classification in Alzheimer's Disease and Mild Cognitive Impairment Applied on Data from ADNI,” Hippocampus, vol. 19, no. 6, pp. 579-587, Jun. 2009.

  • Related Research Results (1)
  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    City Research Online - IRUS-UK 0 17
Share - Bookmark