SamRobNODDI: <i>q</i>-space sampling-augmented continuous representation learning for robust and generalized NODDI

<i>Objective</i>. Neurite orientation dispersion and density imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods require the number and coordinates of gradient directions during testing and training to remain strictly consistent, significantly limiting the generalization and robustness of these models in NODDI parameter estimation. Therefore, it is imperative to develop methods that can perform robustly under varying diffusion gradient directions.<i>Approach</i>. In this paper, we propose a<i>q</i>-space sampling augmentation-based continuous representation learning framework (SamRobNODDI) to achieve robust and generalized NODDI. Specifically, a continuous representation learning method based on<i>q</i>-space sampling augmentation is introduced to fully explore the information between different gradient directions in<i>q</i>-space. Furthermore, we design a sampling consistency loss to constrain the outputs of different sampling schemes, ensuring that the outputs remain as consistent as possible, thereby further enhancing performance and robustness to varying<i>q</i>-space sampling schemes. SamRobNODDI is also a flexible framework that can be applied to different backbone networks.<i>Main results</i>. SamRobNODDI was compared against seven state-of-the-art methods across 19 diverse<i>q</i>-space sampling schemes. Extensive experimental validations have been conducted under both identical and diverse sampling schemes for training and testing, as well as across varying sampling rates, different loss functions, and multiple network backbones.<i>Significance</i>. Results demonstrate that the proposed SamRobNODDI has better performance, robustness, generalization, and flexibility in the face of varying<i>q</i>-space sampling schemes.