SamRobNODDI: q-space sampling-augmented continuous representation learning for robust and generalized NODDI.

Journal: Physics in medicine and biology
Published Date: Aug 8, 2025

Abstract

OBJECTIVE: 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.

Authors

  • Taohui Xiao
    School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China.
  • Jian Cheng
  • Wenxin Fan
    Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen 518055, China, Shenzhen, Guangdong, 518055, CHINA.
  • Enqing Dong
    School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China; Shandong Intelligent Sensing Electronic Technology Co., Ltd. Weihai, 264209, China. Electronic address: enqdong@sdu.edu.cn.
  • Shanshan Wang
    Key Laboratory of Agri-food Safety and Quality, Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Ministry of Agriculture of China, Beijing, 100081, PR China.

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