Seminars

Medical image analysis based on diffeomorphisms (differentiable
one to one and onto maps with differentiable inverse) has placed
computational analysis of anatomy and physiology on firm theoretical
ground.  We detail our approach to diffeomorphic computational anatomy 
while highlighting both theoretical and practical benefits.  We first
introduce the metric used to locate geodesics in the diffeomorphic space. 
Second, we give a variational energy that parameterizes the image
normalization problem in terms of a geodesic diffeomorphism, enabling a
fundamentally symmetric solution.  This approach to normalization is
extended for optimal template population studies using general imaging 
data.  Finally, we show how the temporal parameterization and large
deformation capabilities of diffeomorphisms make them appropriate for
longitudinal analysis, particularly of neurodegenerative data.