Adjugate diffusion tensors for geodesic tractography in white matter

A. Fuster, T.C.J. Dela Haije, A. Tristán-Vega, B.R. Plantinga, C.-F. Westin, L.M.J. Florack

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

11 Citaten (Scopus)
219 Downloads (Pure)


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. Keywords: Riemannian geometry; Geodesic tractography; Diffusion tensor imaging; Brownian motion; Diffusion generator; Sharpened diffusion tensor
Originele taal-2Engels
Pagina's (van-tot)1-14
Aantal pagina's14
TijdschriftJournal of Mathematical Imaging and Vision
Nummer van het tijdschrift1
StatusGepubliceerd - 1 jan 2016


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