MRI Mouse Brain Data of Ischemic Lesion after Transient Middle Cerebral Artery Occlusion

Journal Article (2017)
Author(s)

Inge A. Mulder (Leiden University Medical Center)

Artem Khmelinskii (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, University of Twente, Leiden University Medical Center)

O. Dzyubachyk (Leiden University Medical Center)

Sebastiaan De Jong (Max Planck Institute for Metabolism Research)

Marieke J.H. Wermer (Leiden University Medical Center)

Mathias Hoehn (Leiden University Medical Center, Max Planck Institute for Metabolism Research, University of Twente)

Boudewijn P.F. Lelieveldy (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

AMJM van den Maagdenberg (Leiden University Medical Center)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2017 Inge A. Mulder, Artem Khmelinskii, Oleh Dzyubachyk, Sebastiaan De Jong, Marieke J.H. Wermer, Mathias Hoehn, B.P.F. Lelieveldt, Arn M.J.M. van den Maagdenberg
DOI related publication
https://doi.org/10.3389/fninf.2017.00051
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 Inge A. Mulder, Artem Khmelinskii, Oleh Dzyubachyk, Sebastiaan De Jong, Marieke J.H. Wermer, Mathias Hoehn, B.P.F. Lelieveldt, Arn M.J.M. van den Maagdenberg
Research Group
Pattern Recognition and Bioinformatics
Volume number
11
Pages (from-to)
1-4
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Abstract

In this data report we make available to the community a highly variable longitudinal MRI mouse brain data set of ischemic lesion after transient middle cerebral artery occlusion (tMCAo). Together with the provided semi-automated and automated segmentations, these data can be used to further improve the method proposed by Mulder et al. (2017) and also to serve as a benchmark for comparison between different approaches to segment ischemic lesions in MRI mouse brain data. It can also be used to develop and validate algorithms that further classify the stroke area into core and penumbra.
• The data were collected from mice: (i) of different ages, (ii) of two different strains, (iii) at different time points after the ischemic infarct induction, (iv) from two laboratories, (v) using two different MRI systems, and (vi) using three different sets of acquisition parameters.
• Segmentations of the ischemic lesions are provided as well. These were obtained by: (i) two observers using a semi-automated method and (ii) using the novel automated segmentation approach described by Mulder et al. (2017).
• Type/format of data: raw files, MetaImage files, text/Excel files, analyzed data.
• The following set of images associated with each of the 121 scans is included: raw Bruker MRI data (reference scan, T2 scan with all echoes, calculated T2-weighted map), automated segmentations of the ischemic lesions and semi-automated segmentations by two observers.
• For 99 of these scans, an accompanying set of Bruker MR diffusion maps, containing the Diffusion-Weighted Image (DWI) and calculated Apparent Diffusion Coefficient (ADC) maps, is included.
• Acquisition hardware: small-animal Bruker MRI systems (7 T and 11.7 T).
• Experimental set-up: infarct was induced in male mice of different age and background, using the tMCAo model. After that, MRI scans at different time points after infarct induction were acquired.
• Data sources: Leiden, Netherlands; Cologne, Germany.
• Data accessibility: all related data sets (121 T2 scans + template + 99 diffusion scans) were deposited in the public Dryad Digital Repository (https://doi.org/10.5061/dryad.1m528).