FOD-Swin-Net: angular super resolution of fiber orientation distribution
  using a transformer-based deep model
Mateus Oliveira da Silva Caio Pinheiro Santana Diedre Santos do Carmo Letícia Rittner
Identifying and characterizing brain fiber bundles can help to understand
many diseases and conditions. An important step in this process is the
estimation of fiber orientations using Diffusion-Weighted Magnetic Resonance
Imaging (DW-MRI). However, obtaining robust orientation estimates demands
high-resolution data, leading to lengthy acquisitions that are not always
clinically available. In this work, we explore the use of automated angular
super resolution from faster acquisitions to overcome this challenge. Using the
publicly available Human Connectome Project (HCP) DW-MRI data, we trained a
transformer-based deep learning architecture to achieve angular super
resolution in fiber orientation distribution (FOD). Our patch-based
methodology, FOD-Swin-Net, is able to bring a single-shell reconstruction
driven from 32 directions to be comparable to a multi-shell 288 direction FOD
reconstruction, greatly reducing the number of required directions on initial
acquisition. Evaluations of the reconstructed FOD with Angular Correlation
Coefficient and qualitative visualizations reveal superior performance than the
state-of-the-art in HCP testing data. Open source code for reproducibility is
available at https://github.com/MICLab-Unicamp/FOD-Swin-Net.