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.

翻译：识别和表征脑纤维束有助于理解多种疾病与病理状态。该过程中的关键步骤是利用弥散加权磁共振成像（DW-MRI）估计纤维取向。然而，获取稳健的取向估计需要高分辨率数据，导致采集时间漫长，难以在临床中常规应用。本研究探索采用快速采集的自动角度超分辨率技术应对这一挑战。基于公开的人类连接组计划（HCP）DW-MRI数据，我们训练了一种基于Transformer的深度学习架构，以实现纤维取向分布（FOD）的角度超分辨率。本研究的基于图像块的FOD-Swin-Net方法，能将单壳层32个方向的FOD重建提升至与多壳层288个方向FOD重建相当的水平，从而大幅减少初始采集所需的方向数。通过角相关系数对重建FOD进行评估及定性可视化显示，在HCP测试数据上，该方法的性能优于现有技术。可复现的开源代码已发布于https://github.com/MICLab-Unicamp/FOD-Swin-Net。