One of the approaches in diffusion tensor imaging is to consider a
Riemannian metric given by the inverse diffusion tensor. Such a
metric is used for geodesic tractography and connectivity analysis
in white matter. We propose a metric tensor given by the adjugate
rather than the previously proposed inverse diffusion tensor. The
adjugate metric can also be employed in the sharpening framework.
Tractography experiments on synthetic and real brain diffusion data
show improvement for high-curvature tracts and in the vicinity of
isotropic diffusion regions relative to most results for inverse
(sharpened) diffusion tensors, and especially on real data. In
addition, adjugate tensors are shown to be more robust to noise.