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This chapter presents the development and application of 3D navigator-based multi-shot acquisition 3D oscillating gradient prepared gradient and spin echo (3D OGprep-GRASE) and pulsed gradient prepared gradient and spin echo (3D PGprep-GRASE) sequences for whole-brain high-resolution time-dependent diffusion MRI (TDDMRI) on a clinical 3 T system. A key innovation is the integration of a 3D navigator to effectively correct inter-segment phase errors inherent in multi-shot acquisitions, outperforming 1D and 2D navigators. The sequences also incorporate 3D GRAPPA acceleration in both phase-encoding directions to enhance acquisition efficiency and image quality. This allowed for the detailed investigation of the diffusion time dependence of ADC in the cerebral cortex gray matter at high resolution. The study utilized a power-law model to characterize this dependence, yielding an exponent θ, which showed regional variations across the cortex. Furthermore, a correlation analysis revealed a negative relationship between θ and microstructural parameters derived from NODDI (ICVF, ODI) and DKI (MK, RK) models, suggesting links between microstructural complexity and diffusion characteristics.

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3D Navigator based 3D Gradient Spin Echo Imaging

  • 章节

  • 发表日期：

    2025年

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  • Dan Wu,
  • Haotian Li,
  • Qinfeng Zhu,
  • Xingzhou Chen,
  • Li-Ang Xu

摘要

This chapter presents the development and application of 3D navigator-based multi-shot acquisition 3D oscillating gradient prepared gradient and spin echo (3D OGprep-GRASE) and pulsed gradient prepared gradient and spin echo (3D PGprep-GRASE) sequences for whole-brain high-resolution time-dependent diffusion MRI (TDDMRI) on a clinical 3 T system. A key innovation is the integration of a 3D navigator to effectively correct inter-segment phase errors inherent in multi-shot acquisitions, outperforming 1D and 2D navigators. The sequences also incorporate 3D GRAPPA acceleration in both phase-encoding directions to enhance acquisition efficiency and image quality. This allowed for the detailed investigation of the diffusion time dependence of ADC in the cerebral cortex gray matter at high resolution. The study utilized a power-law model to characterize this dependence, yielding an exponent θ, which showed regional variations across the cortex. Furthermore, a correlation analysis revealed a negative relationship between θ and microstructural parameters derived from NODDI (ICVF, ODI) and DKI (MK, RK) models, suggesting links between microstructural complexity and diffusion characteristics.