TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging
This addresses a domain-specific issue for neuroscience and clinical research by enabling the use of disrupted DTI data in group studies, though it appears incremental as it builds on existing inpainting methods tailored to DTI.
The paper tackled the problem of missing slices in Diffusion Tensor Imaging (DTI) due to sub-optimal acquisitions, proposing a novel 3D Tensor-Wise Brain-Aware Gate network (TW-BAG) that reconstructs disrupted DTIs and recovers clinical imaging information, as evaluated on the Human Connectome Project dataset.
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.