An extragradient and noise-tuning adaptive iterative network for diffusion MRI-based microstructural estimation

Diffusion MRI (dMRI) is a powerful technique for investigating tissue microstructure properties. However, advanced dMRI models are typically complex and nonlinear, requiring a large number of acquisitions in the q-space. Deep learning techniques, specifically optimization-based networks, have been proposed to improve the model fitting with limited q-space data. Previous optimization procedures relied on the empirical selection of iteration block numbers and the network structures were based on the iterative hard thresholding (IHT) algorithm, which may suffer from instability during sparse reconstruction. In this study, we introduced an extragradient and noise-tuning adaptive iterative network, a generic network for estimating dMRI model parameters. We proposed an adaptive mechanism that flexibly adjusts the sparse representation process, depending on specific dMRI models, datasets, and downsampling strategies, avoiding manual selection and accelerating inference. In addition, we proposed a noise-tuning module to assist the network in escaping from local minimum/saddle points. The network also included an additional projection of the extragradient to ensure its convergence. We evaluated the performance of the proposed network on the neurite orientation dispersion and density imaging (NODDI) model and diffusion basis spectrum imaging (DBSI) model on two 3T Human Connectome Project (HCP) datasets and a 7T HCP dataset with six different downsampling strategies. The proposed framework demonstrated superior accuracy and generalizability compared to other state-of-the-art microstructural estimation algorithms.