Nonlinear Elastic Spline Registration: Evaluation with Longitudinal Huntington's Disease Data

Longitudinal brain image studies quantify the changes happening over time. Jacobian maps, which characterize the volume change, are based on non-rigid registration techniques and do not always appear to be clinically plausible. In particular, extreme values of volume change are not expected to be seen. The Free-Form Deformation (FFD) algorithm suffers from this drawback. Different penalty terms have been proposed in the past. We present in this paper a regularisation of the B-Spline displacements using nonlinear elasticity. Our work links a finite element method with pseudo-forces derived from a similarity measure. The presented method has been evaluated on longitudinal T1-weighted MR images of Huntington's disease subjects and controls. Multiple time point consistency, the Jacobian map homogeneity and statistical power for group separation have been used. Our new method performs better than the classical FFD, while keeping similar registration accuracy.

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