An Adaptive Network with Extragradient for Diffusion MRI-Based Microstructure Estimation

Diffusion MRI (dMRI) is a powerful tool for probing tissue microstructural properties. However, advanced dMRI models are commonly nonlinear and complex, which requires densely sampled q-space and is prone to estimation errors. This problem can be resolved using deep learning techniques, especially optimization-based networks. In previous optimization-based methods, the number of iterative blocks was selected empirically. Furthermore, previous network structures were based on the iterative shrinkage-thresholding algorithm (ISTA), which could result in instability during sparse reconstruction. In this work, we proposed an adaptive network with extragradient for diffusion MRI-based microstructure estimation (AEME) by introducing an additional projection of the extragradient, such that the convergence of the network can be guaranteed. Meanwhile, with the adaptive iterative selection module, the sparse representation process can be modeled flexibly according to specific dMRI models. The network was evaluated on the neurite orientation dispersion and density imaging (NODDI) model on a public 3T and a private 7T dataset. AEME showed superior improved accuracy and generalizability compared to other state-of-the-art microstructural estimation algorithms.