Spherical Harmonics Representation Learning for High-Fidelity and Generalizable Super-Resolution in Diffusion MRI.

Journal: IEEE transactions on bio-medical engineering
Published Date: Apr 1, 2026

Abstract

OBJECTIVE: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features. Furthermore, traditional pixel-wise loss functions only consider pixel differences, and struggle to recover intricate image details essential for high-resolution reconstruction. METHOD: We propose SHRL-dMRI, a novel Spherical Harmonics Representation Learning framework for high-fidelity, generalizable super-resolution in dMRI to address these challenges. SHRL-dMRI explores implicit neural representations and spherical harmonics to model continuous spatial and angular representations, simultaneously enhancing both spatial and angular resolution while improving the accuracy of microstructural parameter estimation. To further preserve image fidelity, a data-fidelity module and wavelet-based frequency loss are introduced, ensuring the super- resolved images preserve image consistency and retain fine details. RESULTS: Extensive experiments demonstrate that, compared to five other state-of-the-art methods, our method significantly enhances dMRI data resolution, improves the accuracy of microstructural parameter estimation, and provides better generalization capabilities. It maintains stable performance even under a 45× downsampling factor. CONCLUSION AND SIGNIFICANCE: The proposed method can effectively improve the resolution of dMRI data without increasing the acquisition time, providing new possibilities for future clinical applications.

Authors

  • Ruoyou Wu
    Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Jian Cheng
  • Cheng Li
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
  • Juan Zou
    School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, 410114, China.
  • Wenxin Fan
    Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen 518055, China, Shenzhen, Guangdong, 518055, CHINA.
  • Xinrui Ma
  • Hua Guo
    Zhumadian Psychiatric Hospital, Zhumadian 463000, Henan, China.
  • Yong Liang
    Institute of Environment and Health, Jianghan University, Wuhan 430056, China.
  • 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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