Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients.

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Radiology

Background:

• Diffusion MRI (dMRI) is crucial for mapping brain microstructure.
• High gradient strength systems enhance dMRI signal-to-noise ratio (SNR) and acquisition flexibility.
• Advanced dMRI enables detailed microstructural imaging.

Purpose of the Study:

• To introduce the MGH Connectome Diffusion Microstructure Dataset (CDMD).
• To provide a comprehensive dMRI dataset for developing and validating new analysis methods.
• To facilitate research in brain microstructural imaging.

Main Methods:

• Acquired dMRI data from 26 healthy participants on a 3T Connectome scanner with 300 mT/m gradient strength and a 64-channel head coil.
• Systematically sampled diffusion parameters including two diffusion times (19 and 49 ms) and eight gradient strengths (30-290 mT/m).
• Acquired data with 32 or 64 diffusion directions and included scan/rescan data for reliability assessment. Preprocessing included corrections for gradient nonlinearity, eddy currents, and susceptibility distortions.

Main Results:

• A comprehensive dMRI dataset (CDMD) was successfully acquired and preprocessed.
• The dataset covers a wide range of diffusion measurement parameters, suitable for advanced microstructural analysis.
• Scan/rescan data confirmed the reliability of the acquired diffusion MRI measurements.

Conclusions:

• The MGH Connectome Diffusion Microstructure Dataset (CDMD) provides a valuable resource for the neuroimaging community.
• It serves as a test bed for developing novel methods in fiber orientation estimation, tractography, and microstructural modeling.
• This dataset will advance the understanding of brain microstructure through diffusion MRI.