  Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly
used in neuroscience and neurological clinical research through a Diffusion
Tensor Imaging (DTI) model. Volumetric scalar metrics including fractional
anisotropy, mean diffusivity, and axial diffusivity can be derived from the DTI
model to summarise water diffusivity and other quantitative microstructural
information for clinical studies. However, clinical practice constraints can
lead to sub-optimal DWI acquisitions with missing slices (either due to a
limited field of view or the acquisition of disrupted slices). To avoid
discarding valuable subjects for group-wise studies, we propose a novel 3D
Tensor-Wise Brain-Aware Gate network (TW-BAG) for inpainting disrupted DTIs.
The proposed method is tailored to the problem with a dynamic gate mechanism
and independent tensor-wise decoders. We evaluated the proposed method on the
publicly available Human Connectome Project (HCP) dataset using common image
similarity metrics derived from the predicted tensors and scalar DTI metrics.
Our experimental results show that the proposed approach can reconstruct the
original brain DTI volume and recover relevant clinical imaging information.
