🚨 Instead of the code in this repository, it is recommended to use the non-negativity-constrained diffusion kurtosis imaging available in, for example, DIPY.
dkmri.py stands for diffusion kurtosis magnetic resonance imaging in Python. It is a Python package for estimating diffusion and kurtosis tensors from diffusion-weighted magnetic resonance data. The estimation is performed using regularized non-linear optimization informed by a fully-connected feed-forward neural network that is trained to learn the mapping from data to kurtosis metrics. Details can be found in the arXiv preprint and source code.
This software can be used from the command line or in a Python interpreter.
- The command-line interface does not require any knowledge about Python.
- Python interface is for people comfortable with basic Python programming.
First, make sure you have installed Python.
If you just want to use the command-line interface, the recommended way of installing dkmri.py is to use pipx:
pipx install dkmri
pipx automatically creates an isolated environment in which the dependencies are installed.
If you want to use the Python interface, you can use pip (you should install dkmri.py in an isolated environment using venv or conda to avoid dependency issues):
pip install dkmri
The command for using dkmri.py is
dkmri.py data bvals bvecs optional-arguments
where data, bvals, and bvecs are the paths of the files containing the
diffusion-weighted data, b-values, and b-vectors, and optional-arguments is
where to define things such as which parameter maps to save.
For example, a command for computing a mean kurtosis map from data.nii.gz and
saving it in mk.nii.gz could be
dkmri.py data.nii.gz bvals.txt bvecs.txt -mask mask.nii.gz -mk mk.nii.gz
To see a full description of the arguments, execute the following:
dkmri.py -h
See the example notebook.
If you have questions, found bugs, or need help, please open an issue on Github.
If you find this repository useful in work that leads to a scientific publication, please cite the arXiv preprint.