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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["diffusion-mri"],"created_at":"2025-02-05T04:46:26.747Z","updated_at":"2026-03-05T05:30:56.049Z","avatar_url":"https://github.com/kerkelae.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003e 🚨 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).\n\n# `dkmri.py`\n\n`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).\n\nThis software can be used from the command line or in a Python interpreter.\n\n- The command-line interface does not require any knowledge about Python.\n- Python interface is for people comfortable with basic Python programming.\n\n## Installation\n\nFirst, make sure you have installed [Python](https://www.python.org/downloads/).\n\nIf 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):\n\n```\npipx install dkmri\n```\n\npipx automatically creates an isolated environment in which the dependencies are installed.\n\nIf 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):\n\n```\npip install dkmri\n```\n\n## Usage example\n\n### Command-line interface\n\nThe command for using `dkmri.py` is\n\n```\ndkmri.py data bvals bvecs optional-arguments\n```\n\nwhere `data`, `bvals`, and `bvecs` are the paths of the files containing the\ndiffusion-weighted data, b-values, and b-vectors, and `optional-arguments` is\nwhere to define things such as which parameter maps to save.\n\nFor example, a command for computing a mean kurtosis map from `data.nii.gz` and\nsaving it in `mk.nii.gz` could be\n\n```\ndkmri.py data.nii.gz bvals.txt bvecs.txt -mask mask.nii.gz -mk mk.nii.gz\n```\n\nTo see a full description of the arguments, execute the following:\n\n```\ndkmri.py -h\n```\n\n### Python interface\n\nSee the [example notebook](https://github.com/kerkelae/dkmri/blob/main/docs/example.ipynb).\n\n## Support\n\nIf you have questions, found bugs, or need help, please open an\n[issue on Github](https://github.com/kerkelae/dkmri/issues).\n\n## Citation\n\nIf you find this repository useful in work that leads to a scientific publication, please cite the [arXiv preprint](https://arxiv.org/abs/2203.07327).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkerkelae%2Fdkmri","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkerkelae%2Fdkmri","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkerkelae%2Fdkmri/lists"}