DBFF-Net: A Dual-Branch Feature Fusion Network for low angular resolution fiber orientation distribution reconstruction

Purpose

Estimation of Fiber Orientation Distribution (FOD) is an essential step in tractography. However, traditional reconstruction methods such as Multi-shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD) are demanding in terms of data quality and hardware equipment, limiting their application to low-angle resolution data. Deep learning has demonstrated significant potential for fiber orientation distribution reconstruction in recent years. Nevertheless, there is still room for improvement in the models, particularly in terms of reconstruction accuracy and the retention of fine details. This study aims to develop an efficient and reliable deep- learning framework to improve the accuracy of fiber orientation distribution reconstruction, namely, the Dual-Branch Feature Fusion Network (DBFF-Net).

Methods

DBFF-Net learns the key features of high angular resolution FOD through a multi-branch network architecture, which incorporates high-quality MSMT-CSD data as the target during the training process, and by fusing multi-scale feature information, significantly improves the FOD reconstruction performance of Low Angular Resolution Diffusion Imaging (LARDI) data.

Results

The experimental results show that DBFF-Net surpasses existing traditional and deep-learning methods across multiple metrics, particularly in the fiber crossing regions and under LARDI data conditions.

Conclusion

DBFF-Net provides an efficient and reliable FOD reconstruction scheme and offers a new white matter fiber imaging tool in clinical and scientific research.