Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI
This work addresses biomarker estimation in medical imaging for newborn subjects, presenting an incremental hybrid approach.
The paper tackled the problem of estimating biomarkers from diffusion weighted MRI by combining deep learning with atlases, resulting in a method that significantly outperforms standard and recent deep learning techniques on a test cohort of 70 newborn subjects, with improved robustness to down-sampling.
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.