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<p>Multi-echo-time (MTE) diffusion MRI (dMRI) offers several advantages over conventional single TE dMRI, including disentanglement of microstructural and compositional differences, reduction of bias in microstructural properties, and addition of sub-cellular T2 measures. However, MTE methods require additional data acquisition and complex model fitting. In this work, we share a comprehensive MTE dMRI dataset acquired from three healthy subjects in ten TE sessions (each with eight TEs: 62-132 ms and repeated measures at the shortest and longest TEs). The dataset includes two b-values (700 and 2000 s/mm<sup>2</sup>) with 30 gradient directions for each b-value and four b = 0 images, with diffusion times fixed (δ/Δ = 15.2/25.2 ms) across b-values and TEs. Preprocessing steps include denoising, corrections for B0 inhomogeneity, eddy current and motion correction, and aligning the DWIs and b-vectors to the first TE session. The dataset quality is validated by SNR and head motion assessments. The usage of the dataset is shown with microstructure metrics and orientation distribution functions across TE sessions, which may facilitate investigation in combined diffusion-relaxometry.</p>

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Multi-TE Diffusion MRI Dataset for Exploring Combined Diffusion-Relaxometry Methods in Microstructure Imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月10日

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  • Paween Wongkornchaovalit,
  • Bingchen Shao,
  • Lingyu Li,
  • Yahong Chen,
  • Hongjian He,
  • Jianhui Zhong,
  • Ting Gong

摘要

Multi-echo-time (MTE) diffusion MRI (dMRI) offers several advantages over conventional single TE dMRI, including disentanglement of microstructural and compositional differences, reduction of bias in microstructural properties, and addition of sub-cellular T2 measures. However, MTE methods require additional data acquisition and complex model fitting. In this work, we share a comprehensive MTE dMRI dataset acquired from three healthy subjects in ten TE sessions (each with eight TEs: 62-132 ms and repeated measures at the shortest and longest TEs). The dataset includes two b-values (700 and 2000 s/mm2) with 30 gradient directions for each b-value and four b = 0 images, with diffusion times fixed (δ/Δ = 15.2/25.2 ms) across b-values and TEs. Preprocessing steps include denoising, corrections for B0 inhomogeneity, eddy current and motion correction, and aligning the DWIs and b-vectors to the first TE session. The dataset quality is validated by SNR and head motion assessments. The usage of the dataset is shown with microstructure metrics and orientation distribution functions across TE sessions, which may facilitate investigation in combined diffusion-relaxometry.