.. image:: https://raw.githubusercontent.com/mrphys/tensorflow-mri/v0.6.0/tools/assets/tfmr_logo.svg?sanitize=true :align: center :scale: 100 % :alt: TFMRI logo
|
|pypi| |build| |docs| |doi|
.. |pypi| image:: https://badge.fury.io/py/tensorflow-mri.svg :target: https://badge.fury.io/py/tensorflow-mri .. |build| image:: https://github.com/mrphys/tensorflow-mri/actions/workflows/build-package.yml/badge.svg :target: https://github.com/mrphys/tensorflow-mri/actions/workflows/build-package.yml .. |docs| image:: https://img.shields.io/badge/api-reference-blue.svg :target: https://mrphys.github.io/tensorflow-mri/ .. |doi| image:: https://zenodo.org/badge/388094708.svg :target: https://zenodo.org/badge/latestdoi/388094708
.. start-intro
TensorFlow MRI is a library of TensorFlow operators for computational MRI. The library has a Python interface and is mostly written in Python. However, computations are efficiently performed by the TensorFlow backend (implemented in C++/CUDA), which brings together the ease of use and fast prototyping of Python with the speed and efficiency of optimized lower-level implementations.
Being an extension of TensorFlow, TensorFlow MRI integrates seamlessly in ML applications. No additional interfacing is needed to include a SENSE operator within a neural network, or to use a trained prior as part of an iterative reconstruction. Therefore, the gap between ML and non-ML components of image processing pipelines is eliminated.
Whether an application involves ML or not, TensorFlow MRI operators can take full advantage of the TensorFlow framework, with capabilities including automatic differentiation, multi-device support (CPUs and GPUs), automatic device placement and copying of tensor data, and conversion to fast, serializable graphs.
TensorFlow MRI contains operators for:
tfmri.coils <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils>_):
coil combination, coil compression and estimation of coil sensitivity
maps.tfmri.convex <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/convex>_):
convex functions (quadratic, L1, L2, Tikhonov, total variation, etc.) and
optimizers (ADMM).tfmri.initializers <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/initializers>_):
neural network initializers, including support for complex-valued weights.tfmri.io <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/io>_):
additional I/O functions potentially useful when working with MRI data.tfmri.layers <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/layers>_):
layers and building blocks for neural networks, including support for
complex-valued weights, inputs and outputs.tfmri.linalg <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg>_):
linear operators specialized for image processing and MRI.tfmri.losses <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/losses>_):
for classification, segmentation and image restoration.tfmri.metrics <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/metrics>_):
for classification, segmentation and image restoration.tfmri.image <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/image>_):
filtering, gradients, phantoms, image quality assessment, etc.tfmri.recon <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon>_):
Cartesian/non-Cartesian, 2D/3D, parallel imaging, compressed sensing.tfmri.sampling <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling>_):
Cartesian masks, non-Cartesian trajectories, sampling density compensation,
etc.tfmri.signal <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal>):
N-dimensional fast Fourier transform (FFT), non-uniform FFT (NUFFT)
(see also TensorFlow NUFFT <https://github.com/mrphys/tensorflow-nufft>),
discrete wavelet transform (DWT), k-space filtering, etc.tfmri.optimize <https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/optimize>_):
gradient descent, L-BFGS... end-intro
.. start-install
You can install TensorFlow MRI with pip:
.. code-block:: console
$ pip install tensorflow-mri
Note that only Linux is currently supported.
TensorFlow Compatibility ^^^^^^^^^^^^^^^^^^^^^^^^
Each TensorFlow MRI release is compiled against a specific version of TensorFlow. To ensure compatibility, it is recommended to install matching versions of TensorFlow and TensorFlow MRI according to the table below.
.. start-compatibility-table
====================== ======================== ============ TensorFlow MRI Version TensorFlow Compatibility Release Date ====================== ======================== ============ v0.22.0 v2.10.x Sep 26, 2022 v0.21.0 v2.9.x Jul 24, 2022 v0.20.0 v2.9.x Jun 18, 2022 v0.19.0 v2.9.x Jun 1, 2022 v0.18.0 v2.8.x May 6, 2022 v0.17.0 v2.8.x Apr 22, 2022 v0.16.0 v2.8.x Apr 13, 2022 v0.15.0 v2.8.x Apr 1, 2022 v0.14.0 v2.8.x Mar 29, 2022 v0.13.0 v2.8.x Mar 15, 2022 v0.12.0 v2.8.x Mar 14, 2022 v0.11.0 v2.8.x Mar 10, 2022 v0.10.0 v2.8.x Mar 3, 2022 v0.9.0 v2.7.x Dec 3, 2021 v0.8.0 v2.7.x Nov 11, 2021 v0.7.0 v2.6.x Nov 3, 2021 v0.6.2 v2.6.x Oct 13, 2021 v0.6.1 v2.6.x Sep 30, 2021 v0.6.0 v2.6.x Sep 28, 2021 v0.5.0 v2.6.x Aug 29, 2021 v0.4.0 v2.6.x Aug 18, 2021 ====================== ======================== ============
.. end-compatibility-table
.. end-install
Visit the docs <https://mrphys.github.io/tensorflow-mri/>_ for guides,
tutorials and the API reference.
If you use this package and something does not work as you expected, please
file an issue <https://github.com/mrphys/tensorflow-mri/issues/new>_
describing your problem. We're here to help!
If you like this software, star the repository! |stars|
.. |stars| image:: https://img.shields.io/github/stars/mrphys/tensorflow-mri?style=social :target: https://github.com/mrphys/tensorflow-mri/stargazers
If you find this software useful in your research, you can cite TensorFlow MRI
using its Zenodo record <https://doi.org/10.5281/zenodo.5151590>_.
In the above link, scroll down to the "Export" section and select your favorite export format to get an up-to-date citation.
Contributions of any kind are welcome! Open an issue or pull request to begin.
FAQs
A collection of TensorFlow add-ons for computational MRI.
We found that tensorflow-mri demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.