FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep model

TL;DR

This paper introduces FOD-Swin-Net, a transformer-based deep learning model that enhances the angular resolution of fiber orientation distributions from limited DW-MRI data, enabling high-quality reconstructions comparable to multi-shell acquisitions.

Contribution

The paper presents a novel patch-based transformer architecture for angular super resolution in FOD estimation, significantly reducing the number of diffusion directions needed in DW-MRI scans.

Findings

Achieves FOD reconstruction comparable to multi-shell data using fewer directions.

Outperforms state-of-the-art methods in HCP dataset evaluations.

Provides open-source code for reproducibility.

Abstract

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.