Difference between revisions of "Projects:longitudinaldwi"
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Subject-specific analysis of image data often includes comparison of follow-up to baseline, or serial staging of progress of disease or for monitoring therapeutic intervention. Key methodological components are intra-subject registration of the set of scans, and analysis of geometric deformations and appearance changes. In DWI data, such analysis includes deformation of the set of DWI with associated correction of tensor orientation or local ODF adjustement. In addition, scalar invariants such as FA, MD, axial and radial diffusivities have to be compared in regions or along tracts at corresponding anatomical locations. | Subject-specific analysis of image data often includes comparison of follow-up to baseline, or serial staging of progress of disease or for monitoring therapeutic intervention. Key methodological components are intra-subject registration of the set of scans, and analysis of geometric deformations and appearance changes. In DWI data, such analysis includes deformation of the set of DWI with associated correction of tensor orientation or local ODF adjustement. In addition, scalar invariants such as FA, MD, axial and radial diffusivities have to be compared in regions or along tracts at corresponding anatomical locations. | ||
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| + | == Constrained Data Decomposition and Regression for Longitudinal Fiber Tract Di�ffusion == | ||
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| + | We have developed a methodology based on constrained PCA (CPCA) for fi�tting age-related changes of white matter di�ffusion of �fiber tracts. CPCA is applied to a functional data analysis (FDA) problem, where diffusion along parametrized fiber tracts, e.g. FA or MD, represent functions of arc-length. Age regression on tract functions reveals a nonlinear trajectory but also age-related changes varying locally along tracts. | ||
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| + | == Application to longitudinal DTI in Huntington Disease == | ||
Revision as of 03:38, 5 April 2011
Home < Projects:longitudinaldwiContents
Longitudinal Analysis of DWI image data
Description
Subject-specific analysis of image data often includes comparison of follow-up to baseline, or serial staging of progress of disease or for monitoring therapeutic intervention. Key methodological components are intra-subject registration of the set of scans, and analysis of geometric deformations and appearance changes. In DWI data, such analysis includes deformation of the set of DWI with associated correction of tensor orientation or local ODF adjustement. In addition, scalar invariants such as FA, MD, axial and radial diffusivities have to be compared in regions or along tracts at corresponding anatomical locations.
Constrained Data Decomposition and Regression for Longitudinal Fiber Tract Di�ffusion
We have developed a methodology based on constrained PCA (CPCA) for fi�tting age-related changes of white matter di�ffusion of �fiber tracts. CPCA is applied to a functional data analysis (FDA) problem, where diffusion along parametrized fiber tracts, e.g. FA or MD, represent functions of arc-length. Age regression on tract functions reveals a nonlinear trajectory but also age-related changes varying locally along tracts.
