High efficient DTI reconstruction network with flexible diffusion directions

Deep learning has been used in diffusion tensor imaging (DTI) to fast reconstruct diffusion parameters. However, diffusion-weighted images (DWIs) as network input must maintain diffusion gradient direction consistency during training and testing for deep-learning-based DTI parameter mapping. A dynamic-convolution-based network was developed to achieve generalized DTI parameter mapping for flexible diffusion gradient directions. This proposed method uses dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. The results indicate that the proposed method can reconstruct high-quality DTI-derived maps from six diffusion gradient directions.