20 March 2015 A transformation similarity constraint for groupwise nonlinear registration in longitudinal neuroimaging studies
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Abstract
Patients with Alzheimer's disease and other brain disorders often show a similar spatial distribution of volume change throughout the brain over time, but 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.
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Greg M. Fleishman, Greg M. Fleishman, Boris A. Gutman, Boris A. Gutman, P. Thomas Fletcher, P. Thomas Fletcher, Paul Thompson, Paul Thompson, } "A transformation similarity constraint for groupwise nonlinear registration in longitudinal neuroimaging studies", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130X (20 March 2015); doi: 10.1117/12.2080841; https://doi.org/10.1117/12.2080841
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