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MICRA : Microstructural image compilation with repeated acquisitions
Koller, Kristin Rudrapatna, Umesh Chamberland, Maxime Raven, Erika P. Parker, Greg D. Tax, Chantal M. W. Drakesmith, Mark Fasano, Fabrizio Owen, David Hughes, Garin
Koller, Kristin
Rudrapatna, Umesh
Chamberland, Maxime
Raven, Erika P.
Parker, Greg D.
Tax, Chantal M. W.
Drakesmith, Mark
Fasano, Fabrizio
Owen, David
Hughes, Garin
Abstract
We provide a rich multi-contrast microstructural MRI dataset acquired on an ultra-strong gradient 3T Connectom MRI scanner comprising 5 repeated sets of MRI microstructural contrasts in 6 healthy human participants. The availability of data sets that support comprehensive simultaneous assessment of test-retest reliability of multiple microstructural contrasts (i.e., those derived from advanced diffusion, multi-component relaxometry and quantitative magnetisation transfer MRI) in the same population is extremely limited. This unique dataset is offered to the imaging community as a test-bed resource for conducting specialised analyses that may assist and inform their current and future research. The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and computed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols. Our data demonstrate high reproducibility of several microstructural MRI measures across scan sessions as shown by intra-class correlation coefficients and coefficients of variation. To illustrate a potential use of the MICRA dataset, we computed sample sizes required to provide sufficient statistical power a priori across different white matter pathways and microstructure measures for different statistical comparisons. We also demonstrate whole brain white matter voxel-wise repeatability in several microstructural maps. The MICRA dataset will be of benefit to researchers wishing to conduct similar reliability tests, power estimations or to evaluate the robustness of their own analysis pipelines.
Keywords
Date
2021
Type
Journal article
Journal
NeuroImage
Book
Volume
225
Issue
Page Range
1-14
Article Number
Article 117406
ACU Department
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
CC BY-NC-ND 4.0
File Access
Open