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用于探索微观结构成像中扩散-弛豫测量组合方法的多回波扩散磁共振成像数据集

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

作者信息

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

机构信息

College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang, China.

International College, Zhejiang University, Zhejiang, China.

出版信息

Sci Data. 2025 Jul 10;12(1):1191. doi: 10.1038/s41597-025-05544-1.

Abstract

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) 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.

摘要

多回波时间(MTE)扩散磁共振成像(dMRI)相对于传统的单回波时间dMRI具有多个优势,包括解开微观结构和成分差异、减少微观结构属性中的偏差以及增加亚细胞T2测量值。然而,MTE方法需要额外的数据采集和复杂的模型拟合。在这项工作中,我们分享了一个全面的MTE dMRI数据集,该数据集是从三名健康受试者身上在十个回波时间(TE)会话中采集的(每个会话有八个TE:62 - 132毫秒,并且在最短和最长TE处进行重复测量)。该数据集包括两个b值(700和2000 s/mm²),每个b值有30个梯度方向以及四张b = 0图像,扩散时间在不同b值和TE之间固定(δ/Δ = 15.2/25.2毫秒)。预处理步骤包括去噪、B0不均匀性校正、涡流和运动校正,以及将扩散加权图像(DWI)和b向量与第一个TE会话对齐。通过信噪比(SNR)和头部运动评估来验证数据集质量。通过跨TE会话的微观结构指标和方向分布函数展示了数据集的使用情况,这可能有助于联合扩散弛豫测量的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff84/12246157/fa2f18bc6b25/41597_2025_5544_Fig1_HTML.jpg

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