Diffusion-MRI (dMRI)
These Jupyter notebooks include diffusion MRI data analysis to calculate Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), Single-Shell 3-tissue Constrained Spherical Deconvolution (SS3T-CSD), and Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD) modeled parametric maps.
Pre-processing Jupyter notebook includes data denoising using DIPY, susceptibility-induced distortion correction using FSL TOPUP and eddy current-induced distortion and motion correction using FSL EDDY.
Note: estimation of DKI, NODDI, and MSMT-CSD modeled parametric maps require diffusion-weighted MRI data acquired for at least three b-values (e.g. 0, 1000, 2000).
Dependencies
Dependencies to use these Jupyter notebooks are:
- DIPY (https://dipy.org/)
- Nipype (https://nipype.readthedocs.io/en/latest/)
- FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL)
- AMICO (https://github.com/daducci/AMICO)
- MRtrix3 (https://www.mrtrix.org/)
Citation
Messinger, A., Sirmpilatze, N., Heuer, K., Loh, K.K., Mars, R.B., Sein, J., Xu, T., Glen, D., Jung, B., Seidlitz, J., Taylor, P., Toro, R., Garza-Villarreal, E.A., Sponheim, C., Wang, X., Benn, R.A., Cagna, B., Dadarwal, R., Evrard, H.C., Garcia-Saldivar, P., Giavasis, S., Hartig, R., Lepage, C., Liu, C., Majka, P., Merchant, H., Milham, M.P., Rosa, M.G.P., Tasserie, J., Uhrig, L., Margulies, D.S., Klink, P.C., 2020. A collaborative resource platform for non-human primate neuroimaging. NeuroImage 117519. https://doi.org/10.1016/j.neuroimage.2020.117519
Example dataset:
In vivo diffusion MRI data (Macaca mulatta brain scanned at Siemens 3T) provided by the Aix-Marseille Université at the PRIMatE Data Exchange (http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html).
