A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies.
Patients with Alzheimer's disease and other brain disorders often show a similar spatial distribution of volume change throughout the brain over time,1,2but this information is not yet used in registration algorithms to refine the quantification of change. Here, we develop a mathematical basis to incorporate that prior information into a longitudinal structural neuroimaging study. We modify the canonical minimization problem for non-linear registration to include a term that couples a collection of registrations together to enforce group similarity. More specifically, throughout the computation we maintain a group-level representation of the transformations and constrain updates to individual transformations to be similar to this representation. The derivations necessary to produce the Euler-Lagrange equations for the coupling term are presented and a gradient descent algorithm based on the formulation was implemented. We demonstrate using 57 longitudinal image pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that longitudinal registration with such a groupwise coupling prior is more robust to noise in estimating change, suggesting such change maps may have several important applications.
Dept. of Bioengineering, University of California, Los Angeles ; Imaging Genetics Center, INI, University of Southern California.
Greg M
GM
Fleishman
Imaging Genetics Center, INI, University of Southern California.
Boris A
BA
Gutman
Scientific Computing and Imaging Institute, University of Utah.
P Thomas
PT
Fletcher
Imaging Genetics Center, INI, University of Southern California.
Paul
P
Thompson
ADNI; Bayesian registration; Nonlinear registration; Tensor Based Morphometry; groupwise registration; longitudinal registration