• DocumentCode
    2153485
  • Title

    Disease-specific probabilistic brain atlases

  • Author

    Thompson, Paul ; Mega, Michael S. ; Toga, Arthur W.

  • Author_Institution
    Lab. of Neuro Imaging, California Univ., Los Angeles, CA, USA
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    227
  • Lastpage
    234
  • Abstract
    Atlases of the human brain, in health and disease, provide a comprehensive framework for understanding brain structure and function. The complexity and variability of brain structure, especially in the gyral patterns of the human cortex, present challenges in creating standardized brain atlases that reflect the anatomy of a population. This paper introduces the concept of a population-based, disease-specific brain atlas that can reflect the unique anatomy and physiology of a particular clinical subpopulation. Based on well-characterized patient groups, disease-specific atlases contain thousands of structure models, composite maps, average templates, and visualizations of structural variability, asymmetry and group-specific differences. They correlate the structural, metabolic, molecular and histologic hallmarks of the disease. Rather than simply fusing information from multiple subjects and sources, new mathematical strategies are introduced to resolve group-specific features not apparent in individual scans. High-dimensional elastic mappings, based on covariant partial differential equations, are developed to encode patterns of cortical variation. In the resulting brain atlas, disease-specific features and regional asymmetries emerge that are not apparent in individual anatomies. The resulting probabilistic atlas can identify patterns of altered structure and function, and can guide algorithms for knowledge-based image analysis, automated image labeling, tissue classification, data mining and functional image analysis
  • Keywords
    biomedical MRI; brain; data mining; diseases; image classification; image coding; medical image processing; partial differential equations; probability; automated image labeling; brain MRI; brain function; brain structure; clinical subpopulation; complexity; cortical variation patterns; covariant partial differential equations; disease-specific probabilistic brain atlases; functional image analysis; health; high-dimensional elastic mappings; individual scans; knowledge-based image analysis; mathematical strategies; tissue classification; variability; well-characterized patient groups; Anatomy; Brain modeling; Data mining; Diseases; Humans; Image analysis; Labeling; Partial differential equations; Physiology; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mathematical Methods in Biomedical Image Analysis, 2000. Proceedings. IEEE Workshop on
  • Conference_Location
    Hilton Head Island, SC
  • Print_ISBN
    0-7695-0737-9
  • Type

    conf

  • DOI
    10.1109/MMBIA.2000.852382
  • Filename
    852382