nusratsharmin / dipy-dti

dipy-dti

This container calculates DTI parameters, based on diffusion MRI data.

Parameters

fdata : str

The name of a nifti file with preprocessed diffusion data.

fbval : str

The name of a text-file with b-values in FSL format.

fbvec : str

The name of a text file with the b-vectors in FSL format.

fmask : str, optional

The name of a nifti file containing boolean mask of locations to analyze. Default: no masking

fit_method: str, optional

Chooses the algorithm to use in fitting: {"WLS" | "OLS" | "NNLS" |

"RESTORE"}. Default: "WLS", which uses weighted least-squares (see [1]_ for details). Choosing "RESTORE" will use an automated outlier-rejection algorithm [2]_

.. [1] Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. S. Chung, Y. Lu, H.G. Roland (2006). Neuroimage 33: 531-541.

.. [2] L.-C. Chang, D.K. Jones, C. Pierpaoli (2005). RESTORE: Robust estimation of tensors by outlier rejection. MRM 53: 1088-1095

Metadata

The mounted input folder should contain a metadata.json file with the following format:

{
"fdata":"HARDI150.nii.gz",
"fbval":"HARDI150.bval",
"fbvec":"HARDI150.bvec",
"fmask":"mask.nii.gz",
"fit_method":"WLS"
}

Where fdata and fit_method are both optional.

Returns

root_tensor.nii.gz : file A nifti file containing the 6 lower diagonal in the Nifti1 asymm format (see dipy.io.utils and http://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h)

root_{fa,md,ad,rd}: files Nifti files containing the FA, Mean, Axial and Radial diffusivity, respectively.

Examples

To run this container use:

docker run --rm -it -v /path/to/data:/input -v /path/to/output/:/output arokem/dipy-dti

Where the folder /path/to/data/ should contain the metadata.json file,

Notes

This uses the dipy.reconst.dti module: http://nipy.org/dipy/reference/dipy.reconst.html#module-dipy.reconst.dti