Although super-resolution diffusion MRI for isotropic volumes has been explored, to date no unsupervised SR techniques have been investigated for anisotropic dMRI. We propose an autoencoder based framework to enhance the through-plane spatial resolution and to replace slice outliers by leveraging existing high-quality datasets. Quantitative evaluation on 31 pre-term subjects show that the proposed framework significantly outperforms conventionally used interpolation methods at the raw data and estimated diffusion tensor maps. This can hence contribute to the depiction of more accurate white matter properties of the developing brain.
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