Learning diffusion MRI fiber orientation distribution functions of the developing human brain

Diffusion MRI of fetal and newborn brains is constrained by short scanning time allowing only a small number of diffusion measurements to be acquired. Methods going beyond the diffusion tensor model require multi-shell and multiple gradient directions in order to unveil more accurate white matter properties. We propose a learning based framework to reconstruct fiber orientation distribution functions from only six diffusion measurements by leveraging existing high-quality datasets. Quantitative evaluation on 15 newborn subjects show that our framework achieves competitive results with state-of-the-art methods. Qualitative evaluation on a fetus shows the model ability to translate to this challenging population.