Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniques, NODDIDeep learning has a great potential for estimating brain tissue micro-structure from diffusion MRI measurements. However, it is hard to understand and interpret how these models work. Therefore, until now these deep learning models have been designed using ad-hoc approaches. In this work, we propose a method for determining the contribution of different measurements to the prediction produced by these deep learning models. We apply this method for estimating the parameters of the NODDI model for the neonatal brain. Results show that this method is highly effective in determining the subsets of the measurements that result in lower estimation error.
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