Diffusion Tensor Registration with Exact Finite Strain Differential (DT-REFIND) is an algorithm that registers diffusion tensor images using full tensor information. Deforming tensor images require us to interpolate AND reorient the tensors in order to be consistent with anatomy. Unfortunately, this reorientation process introduces difficulty in computing the gradient of the objective function.
We propose an algorithm for the diffeomorphic registration of
diffusion tensor images (DTI). Previous DTI registration algorithms using full tensor information suffer from difficulties
in computing the differential of the Finite Strain tensor reorientation strategy. We borrow results from computer vision
to derive an analytical gradient of the objective function. By
leveraging on the closed-form gradient and the one-parameter
subgroups of diffeomorphisms, the resulting registration algorithm is diffeomorphic and fast. Registration of a pair of
128 × 128 × 60 diffusion tensor volumes takes 15 minutes.
We contrast the algorithm with a classic alternative that does
not take into account the reorientation in the gradient computation. We show with 40 pairwise DTI registrations that using
the exact gradient achieves significantly better registration.
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