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/

Zendy Plus

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

Zendy Tools

Zendy Open

Deep learning has a great potential for estimating biomarkers in diffusionweighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are aunique tool for modeling the spatio-temporal variability of biomarkers. In thispaper, we propose the first framework to exploit both deep learning and atlasesfor biomarker estimation in dMRI. Our framework relies on non-linear diffusiontensor registration to compute biomarker atlases and to estimate atlasreliability maps. We also use nonlinear tensor registration to align the atlasto a subject and to estimate the error of this alignment. We use the biomarkeratlas, atlas reliability map, and alignment error map, in addition to the dMRIsignal, as inputs to a deep learning model for biomarker estimation. We use ourframework to estimate fractional anisotropy and neurite orientation dispersionfrom down-sampled dMRI data on a test cohort of 70 newborn subjects. Resultsshow that our method significantly outperforms standard estimation methods aswell as recent deep learning techniques. Our method is also more robust tostronger measurement down-sampling factors. Our study shows that the advantagesof deep learning and atlases can be synergistically combined to achieveunprecedented accuracy in biomarker estimation from dMRI data.

Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI