LONGITUDINAL GROWTH MODELING OF DISCRETE-TIME FUNCTIONS WITH APPLICATION TO DTI TRACT EVOLUTION IN EARLY NEURODEVELOPMENT.
We present a new framework for spatiotemporal analysis of parameterized functions attributed by properties of 4D longitudinal image data. Our driving application is the measurement of temporal change in white matter diffusivity of fiber tracts. A smooth temporal modeling of change from a discrete-time set of functions is obtained with an extension of the logistic growth model to time-dependent spline functions, capturing growth with only a few descriptive parameters. An unbiased template baseline function is also jointly estimated. Solution is demonstrated via energy minimization with an extension to simultaneous modeling of trajectories for multiple subjects. The new framework is validated with synthetic data and applied to longitudinal DTI from 15 infants. Interpretation of estimated model growth parameters is facilitated by visualization in the original coordinate space of fiber tracts.
School of Computing, SCI Institute, University of Utah, Salt Lake City, UT 84112, USA.
Anuja
A
Sharma
INRIA-Asclepios project, 2004 route des Lucioles, 06902 Sophia Antipolis, France.
Stanley
S
Durrleman
UNC Chapel Hill, Department of Psychiatry, Chapel Hill, NC 27599-7160, USA.
John H
JH
Gilmore
School of Computing, SCI Institute, University of Utah, Salt Lake City, UT 84112, USA.
Guido
G
Gerig
diffusion tensor imaging; early brain development; growth functions; longitudinal image data; spatiotemporal modeling