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.. image:: https://people.ee.ethz.ch/~jweine/cmrsim/latest/_images/Logo_cmrsim_moving_light.svg
:align: right
:scale: 150 %
Welcome to CMRsim!
==================================
CMRsim is a MRI - simulation framework that features bloch-simulations as well as analytic signal
evaluations for **moving objects** with an easy to use API written in pure python.
The computationally demanding calculations are implemented in `TensorFlow2`_ and therefore are
seamlessly executable on CPU and GPU environments, while also making efficient use of computational
resources!
.. _TensorFlow2: https://www.tensorflow.org/
What makes CMRsim special?
- CMRsim features bloch-simulations as well as simulations using analytical signal equations and even allows to combine elements of both.
- Convenient support for **moving** particles!
- Modularity that makes to designing comparative simulation experiments convenient.
- Architecture and implementation that allows to easily include your own simulation modules.
- Reproducibility by using versioned and documented open-source software.
- Easily scalable to detailed digital phantoms
Installation
==============
Pip
^^^^
CMRsim can be installed using the python package manager :code:`pip`. The package can be found
in the `registry`_ of the repository hosted on the ETH-Gitlab instance as well as on the python
package index (PyPI).
.. _registry: https://gitlab.ethz.ch/jweine/cmrsim/-/packages
**Versioning:**
Stable release versions are denoted with the ususal :code:`major.minor` notation. Additionally,
development versions are constantly updated on solved issued and can be installed using the
:code:`major.minor.devX` notation
Release versions are published on PyPI, which allows installing cmrsim with:
.. code-block::
pip install cmrsim
Development versions are only available using the extra url:
.. code-block::
pip install cmrsim --extra-index-url https://gitlab.ethz.ch/api/v4/projects/23798/packages/pypi/simple
To install cuda and cdnn for GPU accellaration (requires nvidia drivers to be installed on your system), from version 0.31 you can use:
.. code-block::
pip install cmrsim[cuda]
Docker
^^^^^^^^
For each major release a set of docker images are compiled and published in the
`container registry`_ associated with repository. Within the docker images, all requirements
are installed for simulation deployment.
.. _container registry: https://gitlab.ethz.ch/jweine/cmrsim/container_registry
Furthermore, there are three image-tags available. To use the pull the docker image
use (e.g. for jupyter tag):
.. code-block::
docker pull registry.ethz.ch/jweine/cmrsim/jupyter:latest
CPU
""""
Based on the Tensorflow:...-cpu:latest docker image. Containing all cmrsim dependencies and cmrsim
GPU
""""
Based on the Tensorflow:...-gpu:latest docker image. Containing all cmrsim dependencies and cmrsim
Jupyter
""""""""
Based on the gpu:latest tag, but also includes an installation of jupyter-lab for interactive
simulation / rendering.
:align: right
:scale: 150 %
Welcome to CMRsim!
==================================
CMRsim is a MRI - simulation framework that features bloch-simulations as well as analytic signal
evaluations for **moving objects** with an easy to use API written in pure python.
The computationally demanding calculations are implemented in `TensorFlow2`_ and therefore are
seamlessly executable on CPU and GPU environments, while also making efficient use of computational
resources!
.. _TensorFlow2: https://www.tensorflow.org/
What makes CMRsim special?
- CMRsim features bloch-simulations as well as simulations using analytical signal equations and even allows to combine elements of both.
- Convenient support for **moving** particles!
- Modularity that makes to designing comparative simulation experiments convenient.
- Architecture and implementation that allows to easily include your own simulation modules.
- Reproducibility by using versioned and documented open-source software.
- Easily scalable to detailed digital phantoms
Installation
==============
Pip
^^^^
CMRsim can be installed using the python package manager :code:`pip`. The package can be found
in the `registry`_ of the repository hosted on the ETH-Gitlab instance as well as on the python
package index (PyPI).
.. _registry: https://gitlab.ethz.ch/jweine/cmrsim/-/packages
**Versioning:**
Stable release versions are denoted with the ususal :code:`major.minor` notation. Additionally,
development versions are constantly updated on solved issued and can be installed using the
:code:`major.minor.devX` notation
Release versions are published on PyPI, which allows installing cmrsim with:
.. code-block::
pip install cmrsim
Development versions are only available using the extra url:
.. code-block::
pip install cmrsim --extra-index-url https://gitlab.ethz.ch/api/v4/projects/23798/packages/pypi/simple
To install cuda and cdnn for GPU accellaration (requires nvidia drivers to be installed on your system), from version 0.31 you can use:
.. code-block::
pip install cmrsim[cuda]
Docker
^^^^^^^^
For each major release a set of docker images are compiled and published in the
`container registry`_ associated with repository. Within the docker images, all requirements
are installed for simulation deployment.
.. _container registry: https://gitlab.ethz.ch/jweine/cmrsim/container_registry
Furthermore, there are three image-tags available. To use the pull the docker image
use (e.g. for jupyter tag):
.. code-block::
docker pull registry.ethz.ch/jweine/cmrsim/jupyter:latest
CPU
""""
Based on the Tensorflow:...-cpu:latest docker image. Containing all cmrsim dependencies and cmrsim
GPU
""""
Based on the Tensorflow:...-gpu:latest docker image. Containing all cmrsim dependencies and cmrsim
Jupyter
""""""""
Based on the gpu:latest tag, but also includes an installation of jupyter-lab for interactive
simulation / rendering.
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