Atlas-based Imaging Data Analysis Pipeline (AIDA) for structural and functional MRI of the mouse brain
Information latest Version 2.0
We fully moved to the containerized version of AIDAmri via Docker. All information can be found in the manual above. Please report issues and bugs directly in the issue section of this repository or at gitter (Link below in the contact section).
Download here (you probably have to clone the dataset from the gin repo. The files are annexed files, also use the raw_data folder as the test data).
Mouse MRI data, acquired with Bruker 9.4T - cryo coil setup: adult C57BL7/6 mouse,
T2-weighted (anatomical scan),
DTI (structural connectivity scan),
rs-fMRI (functional connectivity scan).
Information about Version 1.2 (Docker stable release)
Information about Version 1.1.1 (Docker pre-release)
Information about Version 1.1 (Stable)
Information about Version 1.0
or
join our Open Office Hour - each Thursday 3:00 pm (UTC+2) next meeting in 11.01.2024 at 3:00 pm (UTC+2) 
For all other inquiries: Markus Aswendt ([email protected])
GNU General Public License v3.0If you use our software or modify parts of it and use it in other ways, please cite:
Pallast N, Diedenhofen M, Blaschke S, Wieters F, Wiedermann D, Hoehn M, Fink GR, Aswendt M. Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri). Front Neuroinform. 2019 Jun 4;13:42.doi: 10.3389/fninf.2019.00042.
REFERENCES
- Brain Connectivity Toolbox
- Allen Mouse Brain Reference Atlas
- Niftyreg
- Ourselin, et al. (2001). Reconstructing a 3D structure from serial histological sections. Image and Vision Computing, 19(1-2), 25–31.
- Modat, et al. (2014). Global image registration using a symmetric block- matching approach. Journal of Medical Imaging, 1(2), 024003–024003.
- Rueckert, et al.. (1999). Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging, 18(8), 712–721.
- Modat, et al. (2010). Fast free-form deformation using graphics processing units. Computer Methods And Programs In Biomedicine,98(3), 278–284.
- FSL
- M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86, 2009
- S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004
- M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012
- DSIstudio
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