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In MRI, deep neural networks have been proposed to reconstruct diffusionmodel parameters. However, the inputs of the networks were designed for aspecific diffusion gradient scheme (i.e., diffusion gradient directions andnumbers) and a specific b-value that are the same as the training data. In thisstudy, a new deep neural network, referred to as DIFFnet, is developed tofunction as a generalized reconstruction tool of the diffusion-weighted signalsfor various gradient schemes and b-values. For generalization, diffusionsignals are normalized in a q-space and then projected and quantized, producinga matrix (Qmatrix) as an input for the network. To demonstrate the validity ofthis approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI)and for neurite orientation dispersion and density imaging (DIFFnetNODDI). Ineach model, two datasets with different gradient schemes and b-values aretested. The results demonstrate accurate reconstruction of the diffusionparameters at substantially reduced processing time (approximately 8.7 timesand 2240 times faster processing time than conventional methods in DTI andNODDI, respectively; less than 4% mean normalized root-mean-square errors(NRMSE) in DTI and less than 8% in NODDI). The generalization capability of thenetworks was further validated using reduced numbers of diffusion signals fromthe datasets. Different from previously proposed deep neural networks, DIFFnetdoes not require any specific gradient scheme and b-value for its input. As aresult, it can be adopted as an online reconstruction tool for various complexdiffusion imaging.

DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues