dkmri-WIN 0.1.3

Reproducible and efficient diffusion kurtosis imaging in Python.

> рџ’Ў Instead of the code in this repository, it is recommended to use the [non-negativity-constrained](https://doi.org/10.1016/j.neuroimage.2019.116405) diffusion kurtosis imaging available in, for example, [DIPY](https://github.com/dipy/dipy).

# dkmri.py

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](https://arxiv.org/abs/2203.07327) and [source code](https://github.com/kerkelae/dkmri/blob/main/dkmri/dkmri.py).

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.

## Installation

First, make sure you have installed [Python](https://www.python.org/downloads/).

If you just want to use the command-line interface, the recommended way of installing dkmri.py is to use [pipx](https://github.com/pypa/pipx/#install-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](https://pip.pypa.io/en/stable/) (you should install dkmri.py in an isolated environment using [venv](https://docs.python.org/3/library/venv.html) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to avoid dependency issues):

` pip install dkmri `

## Usage example

### Command-line interface

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 `

### Python interface

See the [example notebook](https://github.com/kerkelae/dkmri/blob/main/docs/example.ipynb).

## Support

If you have questions, found bugs, or need help, please open an [issue on Github](https://github.com/kerkelae/dkmri/issues).

## Citation

If you find this repository useful in work that leads to a scientific publication, please cite the [arXiv preprint](https://arxiv.org/abs/2203.07327).