TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging

TL;DR

This paper introduces TW-BAG, a novel neural network designed to inpaint missing slices in diffusion tensor imaging data, improving the reconstruction of brain DTI volumes for clinical analysis.

Contribution

The paper presents a new tensor-wise brain-aware gating network with dynamic mechanisms and independent decoders for DTI inpainting, tailored to handle disrupted diffusion images.

Findings

Effective reconstruction of missing DTI slices demonstrated on HCP dataset.

Improved accuracy in recovering clinical scalar metrics from inpainted DTIs.

Outperforms existing methods in tensor and scalar metric similarity metrics.

Abstract

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.