Geometric methods for brain image registration and signal analysis
Speaker: Anand A. Joshi , University of Southern California
Date: May 19 2008
Time: 11:00AM to 12:00PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, x38005, polina@csail.mit.edu
Relevant URL: Registration and analysis of neuro-imaging data presents a challenging
problem due to the complex folding patterns in the human brain.
Specifically, the cortical surface of the human brain can be modeled as
a highly convoluted 2D surface. Since it is non-flat, the non-euclidean
geometry of the cortex needs to be accounted for while performing
registration and subsequent signal processing of anatomical and
functional signals on the cortex. Techniques from differential geometry
offer a powerful set of tools to deal with the convoluted nature of the
cortex. We present a method based on p-harmonic mapping for performing
cortical surface parameterization. A 2D coordinate system induced by the
flat mapping is then used to compute the surface metric and discretize
derivatives in the surface geometry. For performing inter-subject
cortical registration based on sulcal landmarks, we generalize
thin-plate splines to non-flat surfaces by using covariant derivatives.
We also present an FEM based method for simultaneous parameterization
and registration of sulcal landmarks based on elastic energy
minimization. These can be used to bring surface signals from individual
brains to a common atlas surface. Isotropic and anisotropic diffusion
filtering methods are formulated for processing of the cortical data.
When the surface data is a point-set on the cortex, we propose a method
to quantify its mean and variance with respect to the surface geometry.
The registration techniques presented for surface alignment are then
extended to volumes to perform full surface and volume registration.
This is done by using volumetric harmonic mappings that extend the
surface point correspondence to the cortical brain volume. Finally, the
volumetric registration is refined by using inverse-consistent linear
elastic intensity registration. This set of methods presents a unified
framework for registration and analysis of the brain signals for
inter-subject neuroanatomical studies.
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