English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Statistical analysis of Diffusion Tensor Imaging (DTI) data requires acomputational framework that is both numerically tractable (to account for thehigh dimensional nature of the data) and geometric (to account for thenonlinear nature of diffusion tensors). Building upon earlier studies that haveshown that a Riemannian framework is appropriate to address these challenges,the present paper proposes a novel metric and an accompanying computationalframework for DTI data processing. The proposed metric retains the geometry andthe computational tractability of earlier methods grounded in the affineinvariant metric. In addition, and in contrast to earlier methods, it providesan interpolation method which preserves anisotropy, a central informationcarried by diffusion tensor data.

An anisotropy preserving metric for DTI processing