=======
bonndit
=======
.. image:: https://badge.fury.io/py/bonndit.svg
:target: https://badge.fury.io/py/bonndit
.. image:: https://readthedocs.org/projects/bonndit/badge/?version=latest
:target: https://bonndit.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
The bonndit package contains computational tools for diffusion MRI processing developed at the University of Bonn.
bonndit implements constrained single and multi tissue deconvolution with higher-order tensor fODFs [Ankele17]_, and the extraction of principal fiber directions with low-rank tensor approximation [Schultz08]_. It also includes code for fiber tractography based on higher-order tensor fODFs, and for filtering the resulting set of streamlines. In particular, bonndit implements spatially regularized tracking using joint tensor decomposition or an Unscented Kalman Filter [Gruen23]_. It also contains code from a study in which we compared the strategy of selecting the most suitable number of fiber compartments per voxel to an adaptive model averaging which reduced the model uncertainty [Gruen22]_.
Finally, the package includes code for suitably constrained fitting of the Diffusional Kurtosis (DKI) model, and computation of corresponding invariants [Groeschel16]_.
* Free software: GNU General Public License v3
* Documentation: https://bonndit.readthedocs.io.
Installation
------------
To install bonndit via pip, run the following command
.. code-block:: console
$ pip install bonndit
To install bonndit via conda, run
.. code-block:: console
$ conda install bonndit -c xderes -c conda-forge
Features
--------
An overview of the scripts and functionality included in bonndit is given in `our documentation <https://bonndit.readthedocs.io/en/latest/>`_. It also includes `a tutorial for performing fiber tracking with our code <https://bonndit.readthedocs.io/en/latest/gettingstarted.html>`_.
Reference
----------
If you use our software as part of a scientific project, please cite the corresponding publications. The method implemented in :code:`stdeconv` and :code:`mtdeconv` was first introduced in
.. [Ankele16] Michael Ankele, Lek-Heng Lim, Samuel Groeschel, Thomas Schultz: Fast and Accurate Multi-Tissue Deconvolution Using SHORE and H-psd Tensors. In: Proc. Medical Image Analysis and Computer-Aided Intervention (MICCAI) Part III, pp. 502-510, vol. 9902 of LNCS, Springer, 2016
It was refined and extended in
.. [Ankele17] Michael Ankele, Lek-Heng Lim, Samuel Groeschel, Thomas Schultz: Versatile, Robust, and Efficient Tractography With Constrained Higher-Order Tensor fODFs. In: Int'l J. of Computer Assisted Radiology and Surgery, 12(8):1257-1270, 2017
The methods implemented in :code:`low-rank-k-approx` was first introduced in
.. [Schultz08] Thomas Schultz, Hans-Peter Seidel: Estimating Crossing Fibers: A Tensor Decomposition Approach. In: IEEE Transactions on Visualization and Computer Graphics, 14(6):1635-42, 2008
The methods implemented in :code:`peak-modelling` was first introduced in
.. [Gruen21] Johannes Grün, Gemma van der Voort, Thomas Schultz: Reducing Model Uncertainty in Crossing Fiber Tractography. In proceedings of EG Workshop on Visual Computing for Biology and Medicine, pages 55-64, 2021
Extended in:
.. [Gruen22] Johannes Grün, Gemma van der Voort, Thomas Schultz: Model Averaging and Bootstrap Consensus Based Uncertainty Reduction in Diffusion MRI Tractography. In: Computer Graphics Forum 42(1):217-230, 2023
The regularized tractography methods (joint low-rank and low-rank UKF) were first implemented in :code:`prob-tracking` and introduced in
.. [Gruen23] Johannes Grün, Samuel Gröschel, Thomas Schultz: Spatially Regularized Low-Rank Tensor Approximation for Accurate and Fast Tractography. In NeuroImage 271:120004, 2023
The use of quadratic cone programming to make the kurtosis fit more stable which is implemented in :code:`kurtosis` has been explained in the methods section of
.. [Groeschel16] Samuel Groeschel, G. E. Hagberg, T. Schultz, D. Z. Balla, U. Klose, T.-K. Hauser, T. Nägele, O. Bieri, T. Prasloski, A. MacKay, I. Krägeloh-Mann, K. Scheffler: Assessing white matter microstructure in brain regions with different myelin architecture using MRI. In: PLOS ONE 11(11):e0167274, 2016
PDFs can be obtained from the respective publisher, or the academic homepage of Thomas Schultz: https://cg.cs.uni-bonn.de/person/prof-dr-thomas-schultz
Authors
-------
* **Michael Ankele** - *Constrained spherical deconvolution with tensor fODFs* - [momentarylapse] (https://github.com/momentarylapse)
* **Johannes Grün** - *Fiber tracking with spatial regularization or model averaging* - [JoGruen] (https://github.com/JoGruen)
* **Olivier Morelle** - *Code curation, documentation and testing* [Oli4] (https://github.com/Oli4)
* **Thomas Schultz** - *DKI fitting, supervision and contributions throughout* - [ThomasSchultz] (https://github.com/ThomasSchultz)
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
=======
History
=======
0.3.1 (2023-03-15)
-------------------
* Included UKF tractography
* Included regularized tractography
0.2.0 (2021-09-17)
-------------------
* Included the missing steps of the whole tracking pipeline.
0.1.2 (2019-02-26)
-------------------
* 'mtdeconv': If response is available, files needed for the computation of the response are not loaded.
0.1.1 (2019-02-06)
-------------------
* First release on PyPI.
0.1.0 (2019-02-06)
------------------
* Making repository public on Github
Raw data
{
"_id": null,
"home_page": "https://github.com/MedVisBonn/bonndit",
"name": "bonndit",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "bonndit",
"author": "Johannes Gruen",
"author_email": "jgruen@uni-bonn.de",
"download_url": "https://files.pythonhosted.org/packages/86/f3/53316ae704e0b3f791972319575d0463206d748e01e42c97d92670f5739c/bonndit-0.3.1.tar.gz",
"platform": null,
"description": "=======\nbonndit\n=======\n\n\n.. image:: https://badge.fury.io/py/bonndit.svg\n :target: https://badge.fury.io/py/bonndit\n\n.. image:: https://readthedocs.org/projects/bonndit/badge/?version=latest\n :target: https://bonndit.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\nThe bonndit package contains computational tools for diffusion MRI processing developed at the University of Bonn.\n\nbonndit implements constrained single and multi tissue deconvolution with higher-order tensor fODFs [Ankele17]_, and the extraction of principal fiber directions with low-rank tensor approximation [Schultz08]_. It also includes code for fiber tractography based on higher-order tensor fODFs, and for filtering the resulting set of streamlines. In particular, bonndit implements spatially regularized tracking using joint tensor decomposition or an Unscented Kalman Filter [Gruen23]_. It also contains code from a study in which we compared the strategy of selecting the most suitable number of fiber compartments per voxel to an adaptive model averaging which reduced the model uncertainty [Gruen22]_.\n\nFinally, the package includes code for suitably constrained fitting of the Diffusional Kurtosis (DKI) model, and computation of corresponding invariants [Groeschel16]_.\n\n\n* Free software: GNU General Public License v3\n* Documentation: https://bonndit.readthedocs.io.\n\nInstallation\n------------\nTo install bonndit via pip, run the following command\n\n.. code-block:: console\n\n $ pip install bonndit\n\nTo install bonndit via conda, run\n\n.. code-block:: console\n\n $ conda install bonndit -c xderes -c conda-forge\n \nFeatures\n--------\nAn overview of the scripts and functionality included in bonndit is given in `our documentation <https://bonndit.readthedocs.io/en/latest/>`_. It also includes `a tutorial for performing fiber tracking with our code <https://bonndit.readthedocs.io/en/latest/gettingstarted.html>`_.\n\nReference\n----------\n\nIf you use our software as part of a scientific project, please cite the corresponding publications. The method implemented in :code:`stdeconv` and :code:`mtdeconv` was first introduced in\n\n.. [Ankele16] Michael Ankele, Lek-Heng Lim, Samuel Groeschel, Thomas Schultz: Fast and Accurate Multi-Tissue Deconvolution Using SHORE and H-psd Tensors. In: Proc. Medical Image Analysis and Computer-Aided Intervention (MICCAI) Part III, pp. 502-510, vol. 9902 of LNCS, Springer, 2016\n\nIt was refined and extended in\n\n.. [Ankele17] Michael Ankele, Lek-Heng Lim, Samuel Groeschel, Thomas Schultz: Versatile, Robust, and Efficient Tractography With Constrained Higher-Order Tensor fODFs. In: Int'l J. of Computer Assisted Radiology and Surgery, 12(8):1257-1270, 2017\n\nThe methods implemented in :code:`low-rank-k-approx` was first introduced in\n\n.. [Schultz08] Thomas Schultz, Hans-Peter Seidel: Estimating Crossing Fibers: A Tensor Decomposition Approach. In: IEEE Transactions on Visualization and Computer Graphics, 14(6):1635-42, 2008\n\nThe methods implemented in :code:`peak-modelling` was first introduced in\n\n.. [Gruen21] Johannes Gr\u00fcn, Gemma van der Voort, Thomas Schultz: Reducing Model Uncertainty in Crossing Fiber Tractography. In proceedings of EG Workshop on Visual Computing for Biology and Medicine, pages 55-64, 2021\n\nExtended in:\n\n.. [Gruen22] Johannes Gr\u00fcn, Gemma van der Voort, Thomas Schultz: Model Averaging and Bootstrap Consensus Based Uncertainty Reduction in Diffusion MRI Tractography. In: Computer Graphics Forum 42(1):217-230, 2023\n\nThe regularized tractography methods (joint low-rank and low-rank UKF) were first implemented in :code:`prob-tracking` and introduced in\n\n.. [Gruen23] Johannes Gr\u00fcn, Samuel Gr\u00f6schel, Thomas Schultz: Spatially Regularized Low-Rank Tensor Approximation for Accurate and Fast Tractography. In NeuroImage 271:120004, 2023\n\n\nThe use of quadratic cone programming to make the kurtosis fit more stable which is implemented in :code:`kurtosis` has been explained in the methods section of\n\n.. [Groeschel16] Samuel Groeschel, G. E. Hagberg, T. Schultz, D. Z. Balla, U. Klose, T.-K. Hauser, T. N\u00e4gele, O. Bieri, T. Prasloski, A. MacKay, I. Kr\u00e4geloh-Mann, K. Scheffler: Assessing white matter microstructure in brain regions with different myelin architecture using MRI. In: PLOS ONE 11(11):e0167274, 2016\n\nPDFs can be obtained from the respective publisher, or the academic homepage of Thomas Schultz: https://cg.cs.uni-bonn.de/person/prof-dr-thomas-schultz\n\nAuthors\n-------\n\n* **Michael Ankele** - *Constrained spherical deconvolution with tensor fODFs* - [momentarylapse] (https://github.com/momentarylapse)\n\n* **Johannes Gr\u00fcn** - *Fiber tracking with spatial regularization or model averaging* - [JoGruen] (https://github.com/JoGruen)\n\n* **Olivier Morelle** - *Code curation, documentation and testing* [Oli4] (https://github.com/Oli4)\n\n* **Thomas Schultz** - *DKI fitting, supervision and contributions throughout* - [ThomasSchultz] (https://github.com/ThomasSchultz)\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.3.1 (2023-03-15)\n-------------------\n* Included UKF tractography\n* Included regularized tractography\n\n0.2.0 (2021-09-17)\n-------------------\n* Included the missing steps of the whole tracking pipeline.\n\n0.1.2 (2019-02-26)\n-------------------\n\n* 'mtdeconv': If response is available, files needed for the computation of the response are not loaded.\n\n0.1.1 (2019-02-06)\n-------------------\n\n* First release on PyPI.\n\n0.1.0 (2019-02-06)\n------------------\n\n* Making repository public on Github\n",
"bugtrack_url": null,
"license": "GNU General Public License v3",
"summary": "The bonndit package contains the latest diffusion imaging tools developed at the University of Bonn.",
"version": "0.3.1",
"split_keywords": [
"bonndit"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "86f353316ae704e0b3f791972319575d0463206d748e01e42c97d92670f5739c",
"md5": "89a72b9260accc818c7349ee7c0c7de9",
"sha256": "b8296939a66d18e298aafb885d101922d2c6a9083a2cf51ea4127964576b6d73"
},
"downloads": -1,
"filename": "bonndit-0.3.1.tar.gz",
"has_sig": false,
"md5_digest": "89a72b9260accc818c7349ee7c0c7de9",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 331479,
"upload_time": "2023-03-27T11:51:54",
"upload_time_iso_8601": "2023-03-27T11:51:54.152166Z",
"url": "https://files.pythonhosted.org/packages/86/f3/53316ae704e0b3f791972319575d0463206d748e01e42c97d92670f5739c/bonndit-0.3.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-03-27 11:51:54",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "MedVisBonn",
"github_project": "bonndit",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "cvxopt",
"specs": [
[
">=",
"1.2.0"
]
]
},
{
"name": "Cython",
"specs": [
[
"==",
"3.0a1"
]
]
},
{
"name": "dipy",
"specs": [
[
">=",
"1.3.0"
]
]
},
{
"name": "findblas",
"specs": []
},
{
"name": "mpmath",
"specs": [
[
"==",
"1.1.0"
]
]
},
{
"name": "pynrrd",
"specs": [
[
"==",
"1.0.0"
]
]
},
{
"name": "nibabel",
"specs": [
[
">=",
"3.0.0"
]
]
},
{
"name": "numpy",
"specs": [
[
">=",
"1.22.0"
]
]
},
{
"name": "pytest",
"specs": [
[
">=",
"6.0.0"
]
]
},
{
"name": "wheel",
"specs": []
},
{
"name": "scipy",
"specs": [
[
">=",
"1.5.0"
]
]
},
{
"name": "setuptools",
"specs": [
[
">=",
"65.6.0"
]
]
},
{
"name": "tqdm",
"specs": [
[
">=",
"4.0.0"
]
]
}
],
"tox": true,
"lcname": "bonndit"
}
JSON
