Quantification of brain lesions using interactive automated software

  • Milan Makale
  • Jeffrey Solomon
  • Nicholas J. Patronas
  • Adrian Danek
  • John A. Butman
  • Jordan Grafman
Article

Abstract

We developed an interactive program, Analysis of Brain Lesions (ABLe) so that researchers studying the effects of brain lesions on cognition could have a user-friendly tool that could quantitatively characterize such lesions. The program was prepared in Tcl/Tk and will run on any UNIX or PC LINUX platform with the MEDx medical imaging software package. The ABLe is almost completely automated and determines the brain lesion size as well as which cytoarchitectonic brain regions (Brodmann areas) are contained within the boundaries of the lesion. Lesion data from multiple subjects can be grouped together and the degree of lesion overlap displayed. All images are analyzed and displayed within standard Talairach coordinate space, and the precision of the match between the ABLe Brodmann area graphics and the subject/patient brain is easily confirmed. The program is the first easy-to-use software that contains these specific features and is available for interested researchers with a background in lesion analysis.

References

  1. Ashburner, J., &Friston, K. J. (1999). Nonlinear spatial normalization using basis functions.Human Brain Mapping,7, 254–266.PubMedCrossRefGoogle Scholar
  2. Braak, H. (1980).Architectonics of the human telencephalic cortex. New York: Springer-Verlag.Google Scholar
  3. Brett, M., Leff, A., Rorden, G., &Ashburner, J. (2001). Spatialnormalisation of brain images with focal lesions using cost function masking.NeuroImage,14, 486–500.PubMedCrossRefGoogle Scholar
  4. Brodmann, K. (1909).Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf grund des Zellenbaues. Leipzig: Barth.Google Scholar
  5. Christensen, G. E., Rabbitt, R. D., &Miller, M. I. (1996). Deformable templates using large deformation kinematics.IEEE Transactions on Image Processing,5, 1435–1447.PubMedCrossRefGoogle Scholar
  6. Collins, D. L., Peters, T. M., &Evans, A. C. (1994). An automated 3D non-linear image deformation procedure for determination of gross morphometric variability in the human brain.Proceedings on Visualization in Biomedical Computing (SPIE),3, 180–190.Google Scholar
  7. Damasio, H. (1995).Brain anatomy in computerized images. New York: Oxford University Press.Google Scholar
  8. Damasio, H., &Damasio, A. R. (1989).Lesion analysis in neuropsychology. New York: Oxford University Press.Google Scholar
  9. Davatzikos, C. (1996). Spatial normalization of 3D brain images using deformable models.Journal of Computer Assisted Tomography,20, 656–665.PubMedCrossRefGoogle Scholar
  10. Duvernoy, H. (1991).The human brain: Surface three-dimensional sectional anatomy and MRI. New York: Springer-Verlag.Google Scholar
  11. Everitt, B. S. (1994).Statisticalmethods for medical investigators (2nd ed.). London: Edward Arnold.Google Scholar
  12. Fiez, J. C., Damasio, H., &Grabowski, T. J. (2000). Lesion segmentation and manual warping to a reference brain: Intra- and interobserver reliability.Human Brain Mapping,9, 192–211.PubMedCrossRefGoogle Scholar
  13. Frank, R. J., Damasio, H., &Grabowski, T. J. (1997). Brainvox: An interactive, multimodal visualization and analysis system for neuroanatomical imaging.Neurolmage,5, 13–30.CrossRefGoogle Scholar
  14. Joshi, M., Cui, J., Doolittle, K., Joshi, S., Van Essen, D., Wang, L., &Miller, M. I. (1999). Brain segmentation and the generation of cortical surfaces.Neurolmage,9, 461–476.CrossRefGoogle Scholar
  15. Lancaster, J. L., Woldoroff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., Kochunov, D., Nickerson, D., Mkiten, S. A., &Fox, P. T. (2000). Automated Talairach atlas labels for functional brain mapping.Human Brain Mapping,10, 120–131.PubMedCrossRefGoogle Scholar
  16. Mazziotta, J. C., Toga, A.W., Evans, A. C., Fox, P., &Lancaster, J. (1995). A probabilistic atlas of the human brain: Theory and rationale for its development.Neurolmage,2, 89–101.CrossRefGoogle Scholar
  17. Nowinski, W. L., Fang, A., Nguyen, B. T., Raphel, J. K., Jagannathan, L., Raghavan, R., Bryan, R. N., &Miller, G. A. (1997). Multiple brain atlas database and atlas-based neuroimaging system.Computer Aided Surgery,2, 42–66.PubMedCrossRefGoogle Scholar
  18. Roland, P. E., &Zilles, K. (1998). Structural divisions and functional fields in the human cerebral cortex.Brain Research Reviews,26, 87–105.PubMedCrossRefGoogle Scholar
  19. Smith, S. (2000). Robust automated brain extraction. In P. T. Fox & J. L. Lancaster (Eds.),Sixth International Conference on Functional Mapping of the Human Brain — Proceedings. San Diego: Academic Press.Google Scholar
  20. Talairach, J., &Szikla, G. (1967).Atlas d’anatomie stereotaxique du telencephale: Études anatomo-radiologiques. Paris: Masson et Cie.Google Scholar
  21. Talairach, J., &Tournoux, P. (1988).Co-planar stereotaxic atlas of the human brain. New York: Thiem.Google Scholar
  22. Thompson, P., &Toga, A. (1996). A surface based technique for warping three-dimensional images of the brain.IEEE Transactions on Medical Imaging,15, 402–417.PubMedCrossRefGoogle Scholar
  23. Woods, R. P., Cherry, S. R., &Mazziotta, J. C. (1992). Rapid automated algorithmfor aligning and reslicing PET images.Journal of Computer Assisted Tomogaphy,16, 620–633.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2002

Authors and Affiliations

  • Milan Makale
    • 1
    • 3
  • Jeffrey Solomon
    • 2
  • Nicholas J. Patronas
    • 3
  • Adrian Danek
    • 3
  • John A. Butman
    • 3
  • Jordan Grafman
    • 3
  1. 1.H. M. Jackson FoundationBethesda
  2. 2.Sensor Systems IncorporatedSterling
  3. 3.Cognitive Neuroscience Section, National Institute for Neurological Disorders and StrokeNational Institutes of HealthBethesda

Personalised recommendations