DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues
This work addresses the problem of inflexible deep learning models for MRI diffusion parameter reconstruction, offering a generalized and faster tool for researchers and clinicians working with diverse diffusion imaging protocols.
This paper introduces DIFFnet, a deep neural network designed to reconstruct diffusion model parameters from MRI data. It generalizes across various diffusion gradient schemes and b-values, achieving significantly faster processing times (8.7x faster for DTI, 2240x faster for NODDI) with low error rates (less than 4% NRMSE for DTI, less than 8% for NODDI) compared to conventional methods.
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.