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Objective: Most deep neural network-based diffusion tensor imaging methodsrequire the diffusion gradients' number and directions in the data to bereconstructed to match those in the training data. This work aims to developand evaluate a novel dynamic-convolution-based method called FlexDTI for highlyefficient diffusion tensor reconstruction with flexible diffusion encodinggradient scheme. Approach: FlexDTI was developed to achieve high-quality DTIparametric mapping with flexible number and directions of diffusion encodinggradients. The method used dynamic convolution kernels to embed diffusiongradient direction information into feature maps of the corresponding diffusionsignal. Furthermore, it realized the generalization of a flexible number ofdiffusion gradient directions by setting the maximum number of input channelsof the network. The network was trained and tested using datasets from theHuman Connectome Project and local hospitals. Results from FlexDTI and otheradvanced tensor parameter estimation methods were compared. Main results:Compared to other methods, FlexDTI successfully achieves high-quality diffusiontensor-derived parameters even if the number and directions of diffusionencoding 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 flexiblediffusion encoding gradient scheme. Significance: FlexDTI can well learndiffusion gradient direction information to achieve generalized DTIreconstruction with flexible diffusion gradient scheme. Both flexibility andreconstruction quality can be taken into account in this network.

FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning