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Implementation of "Automated characterization of noise distributions in diffusion MRI data".

Project description

Automated characterization of noise distributions in diffusion MRI data

The example and documentation

The latest version can be installed with

pip install autodmri

You can find a quick example and datasets over here and the full documentation at http://autodmri.rtfd.io/.

An example, super basic call to the script would be

autodmri_get_distribution dwi.nii.gz sigma.nii.gz N.nii.gz mask.nii.gz

Be sure to check the options by passing --help to the script.

The manuscript and references

You can read the journal version in Medical Image Analysis and the datasets are available here https://zenodo.org/record/2483105.

The conference version of the manuscript (as published in MICCAI 2018) is available here for free and from the publisher.

Here is a bibtex entry for the journal version

@article{St-jean2020_media,
author = {St-Jean, Samuel and {De Luca}, Alberto and Tax, Chantal M.W. and Viergever, Max A. and Leemans, Alexander},
doi = {10.1016/j.media.2020.101758},
eprint = {1906.12121},
issn = {13618415},
journal = {Medical Image Analysis},
month = {jun},
pages = {101758},
title = {{Automated characterization of noise distributions in diffusion MRI data}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841520301225},
year = {2020}
}

and for the conference manuscript in MICCAI

@InProceedings{St-jean2018_miccai,
author="St-Jean, Samuel
and De Luca, Alberto
and Viergever, Max A.
and Leemans, Alexander",
editor="Frangi, Alejandro F.
and Schnabel, Julia A.
and Davatzikos, Christos
and Alberola-L{\'o}pez, Carlos
and Fichtinger, Gabor",
title="Automatic, Fast and Robust Characterization of Noise Distributions for Diffusion MRI",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="304--312",
isbn="978-3-030-00928-1"
}

Referencing the code itself

The code is also autoarchived on zenodo for those wanting to refer to a specific version over here

DOI

Project details


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