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

arXiv: https://arxiv.org/abs/2402.11775 · HTML: https://mlsanity.com/paper/2402.11775/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [TractSeg - Fast and accurate white matter tract segmentation](https://mlsanity.com/paper/1805.07103/) (2018)
- [Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture](https://mlsanity.com/paper/2307.15273/) (2023)

## Built on by (most cited first, 4 of 4)

- [Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI](https://mlsanity.com/paper/2409.13846/) (2024)
- [Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains](https://mlsanity.com/paper/2409.01195/) (2024)
- [From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement](https://mlsanity.com/paper/2508.10950/) (2025)
- [FOD-Diff: 3D Multi-Channel Patch Diffusion Model for Fiber Orientation Distribution](https://mlsanity.com/paper/2512.16075/) (2025)

## Abstract (arXiv)

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.