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Computational Atlases of Severity of White Matter Lesions in Elderly Subjects with MRI

  • Stathis Hadjidemetriou
  • Peter Lorenzen
  • Norbert Schuff
  • Susanne Mueller
  • Michael Weiner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

MRI of cerebral white matter may show regions of signal abnormalities. These changes may be associated with hypertension, inflammation, or ischemia, as well as altered brain function. The goal of this work has been to construct computational atlases of white matter lesions that represent both their severity as well as the frequency of their occurrence in a population to achieve a better classification of white matter disease. An atlas is computed with a pipeline that uses 4T FLAIR and 4T T1-weighted (T1w) brain images of a group of subjects. The processing steps include intensity correction, lesion extraction, intra-subject FLAIR to T1w rigid registration, and seamless replacement of lesions in T1w images with synthetic white matter texture. Subsequently, the T1w images and lesion images of different subjects are registered non-rigidly to the same space. The decrease in T1w intensities is used to obtain severity information. Atlases were constructed for two groups of subjects, elderly normal controls or with mild cognitive impairment, and subjects with cerebrovascular disease. The lesion severities of the two groups have a significant statistical difference with the severity in the atlas of cerebrovascular disease being higher.

Keywords

Intensity correction brain lesion segmentation lesion severity computational atlas construction 

References

  1. 1.
    Erkinjuntti, T., Gao, F., Lee, D.: Lack of difference in brain hyperintensities between patients with early Alzheimer’s disease and control subjects. Arch. Neurol. 51, 260–268 (1994)Google Scholar
  2. 2.
    Pantoni, L., Simoni, M., Pracucci, G.: European task on age-related white matter changes. Visual rating scales for age-related white matter changes (leukoaraiosis). Can the heterogeneity be reduced? Stroke 33, 2827–2833 (2002)Google Scholar
  3. 3.
    Jack, C., O’Brien, P., Rettman, D., Shiung, M., Xu, Y., Muthupillai, R., Manduca, A., Avula, R., Erickson, B.: FLAIR histogram segmentation for measurement of leukoaraiosis volume. Journal of Magnetic Resonance Imaging 14, 668–676 (2001)CrossRefGoogle Scholar
  4. 4.
    Wen, W., Sachdev, P.: The topography of white matter hyperintensities on brain MRI in healthy 60-to 64-year-old individuals. Neuroimage 22, 144–154 (2004)CrossRefGoogle Scholar
  5. 5.
    Yoshita, M., Fletcher, E., DeCarli, C.: Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging. Topics in Magnetic Resonance Imaging 16(6), 399–407 (2005)CrossRefGoogle Scholar
  6. 6.
    Walderveen, M., Kamphorst, W., Scheltens, P.: Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis. Neurology 50, 1282–1288 (1998)Google Scholar
  7. 7.
    Pham, S., Shen, D., Herskovits, E., Resnick, S., Davatzikos, C.: Automatic segmentation of white matter lesions in T1-weighted brain MR images. In: Proc. of ISBI, pp. 253–256 (2002)Google Scholar
  8. 8.
    Ballin, A., Galun, M., Gomori, M., Filippi, M., Valsasina, P., Basri, R., Brandt, A.: An integrated segmentation and classification approach applied to multiple sclerosis analysis. In: Proc. of CVPR, pp. 1122–1129 (2006)Google Scholar
  9. 9.
    Warfield, S., Dengler, J., Zaers, J., Guttmann, C., Wells, W., Ettinger, G., Hiller, J., Kikinis, R.: Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. Journal of Image Guided Surgery 1(6), 326–338 (1995)CrossRefGoogle Scholar
  10. 10.
    Hadjidemetriou, S., Studholme, C., Mueller, S., Weiner, M., Schuff, N.: Restoration of MRI data for field nonuniformities using high order neighborhood statistics. In: Proc. of SPIE Medical Image Processing, vol. 6512 (2007)Google Scholar
  11. 11.
    Smith, S.: Fast robust automated brain extraction. Proc. of Human Brain Mapping 17, 143–155 (2002)CrossRefGoogle Scholar
  12. 12.
    Rovaris, M., Comi, G., Rocca, M., Cercignani, M., Colombo, B., Santuccio, G., Filippi, M.: Relevance of hypointense lesions on fast fluid-attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. American Journal of Neuroradiology 20, 813–820 (1999)Google Scholar
  13. 13.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. on PAMI 23(11), 1222–1239 (2001)Google Scholar
  14. 14.
    Kovalev, V., Kruggel, F., Gertz, H., Cramon, D.: Three-dimensional texture analysis of MRI brain datasets. IEEE Trans. on Medical Imaging 20(5), 424–433 (2001)CrossRefGoogle Scholar
  15. 15.
    Zalesny, A., Ferrari, V., Caenen, G., Gool, L.: Composite texture synthesis. International Journal of Computer Vision 62(1/2), 161–176 (2005)CrossRefGoogle Scholar
  16. 16.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151–S160 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Stathis Hadjidemetriou
    • 1
  • Peter Lorenzen
    • 1
  • Norbert Schuff
    • 2
  • Susanne Mueller
    • 2
  • Michael Weiner
    • 2
  1. 1.NCIRE/VA UCSFSan FranciscoUSA
  2. 2.UCSFSan FranciscoUSA

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