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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

"MASSIVE" brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation.

Martijn Froeling¹, Chantal M W Tax², Sjoerd B Vos^2,3

¹Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands.

Magnetic Resonance in Medicine

|May 14, 2016

Summary

The MASSIVE dataset provides extensive diffusion MRI data from a single subject to advance diffusion modeling techniques. This publicly available resource aids in validating new processing methods and comparing different modeling approaches.

Keywords:

brain dataset diffusion MRI evaluation methods development modeling structural MRI

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Area of Science:

Neuroimaging
Biomedical Engineering

Background:

Diffusion MRI (dMRI) is crucial for understanding brain microstructure.
Developing robust dMRI modeling techniques requires high-quality, standardized datasets.

Purpose of the Study:

To introduce the MASSIVE dataset, a comprehensive resource for dMRI methodology development.
To facilitate validation and comparison of novel diffusion MRI processing and modeling techniques.

Main Methods:

Acquired extensive dMRI data from a single healthy subject over 18 sessions (22.5 hours total).
Utilized a 3 Tesla MRI scanner with an eight-channel head coil.
Acquired dMRI data with isotropic resolution (2.5 mm^3) across multiple shells (b-values up to 9000 s/mm^2).

Main Results:

The dataset comprises 8000 dMRI volumes, B0 field maps, noise maps, and ten 3D FLAIR, T1, and T2-weighted scans.
Achieved an average signal-to-noise ratio of approximately 35 for non-diffusion-weighted images.
Data includes both unprocessed and processed versions.

Conclusions:

The MASSIVE dataset offers a unique in vivo resource for evaluating dMRI processing and modeling.
Provides a framework for reliable comparison of different diffusion modeling approaches.
The dataset is publicly accessible at www.massive-data.org.