  Objective: Most deep neural network-based diffusion tensor imaging methods
require the diffusion gradients' number and directions in the data to be
reconstructed to match those in the training data. This work aims to develop
and evaluate a novel dynamic-convolution-based method called FlexDTI for highly
efficient diffusion tensor reconstruction with flexible diffusion encoding
gradient scheme. Approach: FlexDTI was developed to achieve high-quality DTI
parametric mapping with flexible number and directions of diffusion encoding
gradients. The method used dynamic convolution kernels to embed diffusion
gradient direction information into feature maps of the corresponding diffusion
signal. Furthermore, it realized the generalization of a flexible number of
diffusion gradient directions by setting the maximum number of input channels
of the network. The network was trained and tested using datasets from the
Human Connectome Project and local hospitals. Results from FlexDTI and other
advanced tensor parameter estimation methods were compared. Main results:
Compared to other methods, FlexDTI successfully achieves high-quality diffusion
tensor-derived parameters even if the number and directions of diffusion
encoding gradients change. It reduces normalized root mean squared error
(NRMSE) by about 50% on fractional anisotropy (FA) and 15% on mean diffusivity
(MD), compared with the state-of-the-art deep learning method with flexible
diffusion encoding gradient scheme. Significance: FlexDTI can well learn
diffusion gradient direction information to achieve generalized DTI
reconstruction with flexible diffusion gradient scheme. Both flexibility and
reconstruction quality can be taken into account in this network.
