DOI: 10.57760/sciencedb.o00133.00038
title: Multi-TE Diffusion MRI Dataset for Exploring Combined Diffusion-Relaxometry Methods in Microstructure Imaging
title: 面向大脑微结构扩散-弛豫联合研究的多回波扩散磁共振成像数据集

description: Multi-echo-time (MTE) diffusion magnetic resonance imaging (dMRI) advances conventional dMRI acquired with a single TE by enabling the consideration of both microstructural and compositional differences within tissue compartments, providing less biased microstructural properties and additional sub-cellular T2 measures. Despite that these measures can potentially provide more specific biomarkers, such MTE methods are in the early stage towards clinical research due to additional data requirements and more complex model fitting that requires evaluation. In this work, we share a comprehensive multi-echo dMRI dataset acquired from three healthy subjects across ten TE sessions, with the goal to facilitate research in investigating methods in combined diffusion-relaxometry modelling and their reproducibility. We incorporate eight TEs ranging from 62 ms to 132 ms, with repeated measures at the shortest and longest TEs for each subject. 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 across b-values and TEs. Preprocessing steps, including denoising, B0 inhomogeneity correction, eddy current and motion corrections, and aligning the DWIs and b-vectors to the first TE session were applied to facilitate further analysis. We demonstrate the high quality of this dataset by providing SNR and head motion assessments across subjects. We further demonstrate the usage of the dataset by showing microstructure metrics and orientation distribution functions across TE sessions. We envision that this unique dataset will be valuable for investigating minimal acquisition protocols, model fitting methods and exploring variations of MTE tissue properties across the brain in healthy subjects.
description: Multi-echo-time (MTE) diffusion magnetic resonance imaging (dMRI) advances conventional dMRI acquired with a single TE by enabling the consideration of both microstructural and compositional differences within tissue compartments, providing less biased microstructural properties and additional sub-cellular T2 measures. Despite that these measures can potentially provide more specific biomarkers, such MTE methods are in the early stage towards clinical research due to additional data requirements and more complex model fitting that requires evaluation. In this work, we share a comprehensive multi-echo dMRI dataset acquired from three healthy subjects across ten TE sessions, with the goal to facilitate research in investigating methods in combined diffusion-relaxometry modelling and their reproducibility. We incorporate eight TEs ranging from 62 ms to 132 ms, with repeated measures at the shortest and longest TEs for each subject. 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 across b-values and TEs. Preprocessing steps, including denoising, B0 inhomogeneity correction, eddy current and motion corrections, and aligning the DWIs and b-vectors to the first TE session were applied to facilitate further analysis. We demonstrate the high quality of this dataset by providing SNR and head motion assessments across subjects. We further demonstrate the usage of the dataset by showing microstructure metrics and orientation distribution functions across TE sessions. We envision that this unique dataset will be valuable for investigating minimal acquisition protocols, model fitting methods and exploring variations of MTE tissue properties across the brain in healthy subjects.