A hybrid network for fiber orientation distribution reconstruction employing multi-scale information

Accurate fiber orientation distribution (FOD) is crucial for resolving complex neural fiber structures. However, existing reconstruction methods often fail to integrate both global and local FOD information, as well as the directional information of fixels, which limits reconstruction accuracy. Additionally, these methods overlook the spatial positional relationships between voxels, resulting in extracted features that lack continuity. In regions with signal distortion, many methods also exhibit issues with reconstruction artifacts.

This study addresses these challenges by introducing a new neural network called Fusion-Net.

Fusion-Net comprises both the FOD reconstruction network and the peak direction estimation network. The FOD reconstruction network efficiently fuses the global and local features of the FOD, providing these features with spatial positional information through a competitive coordinate attention mechanism and a progressive updating mechanism, thus ensuring feature continuity. The peak direction estimation network redefines the task of estimating fixel peak directions as a multi-class classification problem. It uses a direction-aware loss function to supply directional information to the FOD reconstruction network. Additionally, we introduce a larger input scale for Fusion-Net to compensate for local signal distortion by incorporating more global information.

Experimental results demonstrate that the rich FOD features contribute to promising performance in Fusion-Net. The network effectively utilizes these features to enhance reconstruction accuracy while incorporating more global information, effectively mitigating the issue of local signal distortion.

This study demonstrates the feasibility of Fusion-Net for reconstructing FOD, providing reliable references for clinical applications.