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