  Deep learning has a great potential for estimating biomarkers in diffusion
weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a
unique tool for modeling the spatio-temporal variability of biomarkers. In this
paper, we propose the first framework to exploit both deep learning and atlases
for biomarker estimation in dMRI. Our framework relies on non-linear diffusion
tensor registration to compute biomarker atlases and to estimate atlas
reliability maps. We also use nonlinear tensor registration to align the atlas
to a subject and to estimate the error of this alignment. We use the biomarker
atlas, atlas reliability map, and alignment error map, in addition to the dMRI
signal, as inputs to a deep learning model for biomarker estimation. We use our
framework to estimate fractional anisotropy and neurite orientation dispersion
from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results
show that our method significantly outperforms standard estimation methods as
well as recent deep learning techniques. Our method is also more robust to
stronger measurement down-sampling factors. Our study shows that the advantages
of deep learning and atlases can be synergistically combined to achieve
unprecedented accuracy in biomarker estimation from dMRI data.
