Package: tensorflow-mri
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:Multicoil arrays
(tfmri.coils):
coil combination, coil compression and estimation of coil sensitivity
maps.Convex optimization
(tfmri.convex):
convex functions (quadratic, L1, L2, Tikhonov, total variation, etc.) and
optimizers (ADMM).Keras initializers
(tfmri.initializers):
neural network initializers, including support for complex-valued weights.I/O (tfmri.io):
additional I/O functions potentially useful when working with MRI data.Keras layers
(tfmri.layers):
layers and building blocks for neural networks, including support for
complex-valued weights, inputs and outputs.Linear algebra
(tfmri.linalg):
linear operators specialized for image processing and MRI.Loss functions
(tfmri.losses):
for classification, segmentation and image restoration.Metrics
(tfmri.metrics):
for classification, segmentation and image restoration.Image processing
(tfmri.image):
filtering, gradients, phantoms, image quality assessment, etc.Image reconstruction
(tfmri.recon):
Cartesian/non-Cartesian, 2D/3D, parallel imaging, compressed sensing.k-space sampling
(tfmri.sampling):
Cartesian masks, non-Cartesian trajectories, sampling density compensation,
etc.Signal processing
(tfmri.signal):
N-dimensional fast Fourier transform (FFT), non-uniform FFT (NUFFT)
(see alsoTensorFlow NUFFT),
discrete wavelet transform (DWT),k-space filtering, etc.Unconstrained optimization
(tfmri.optimize):
gradient descent, L-BFGS.And more, e.g., supporting array manipulation and math tasks.InstallationYou can install TensorFlow MRI withpip:$pipinstalltensorflow-mriNote that only Linux is currently supported.TensorFlow CompatibilityEach 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.TensorFlow MRI VersionTensorFlow CompatibilityRelease Datev0.22.0v2.10.xSep 26, 2022v0.21.0v2.9.xJul 24, 2022v0.20.0v2.9.xJun 18, 2022v0.19.0v2.9.xJun 1, 2022v0.18.0v2.8.xMay 6, 2022v0.17.0v2.8.xApr 22, 2022v0.16.0v2.8.xApr 13, 2022v0.15.0v2.8.xApr 1, 2022v0.14.0v2.8.xMar 29, 2022v0.13.0v2.8.xMar 15, 2022v0.12.0v2.8.xMar 14, 2022v0.11.0v2.8.xMar 10, 2022v0.10.0v2.8.xMar 3, 2022v0.9.0v2.7.xDec 3, 2021v0.8.0v2.7.xNov 11, 2021v0.7.0v2.6.xNov 3, 2021v0.6.2v2.6.xOct 13, 2021v0.6.1v2.6.xSep 30, 2021v0.6.0v2.6.xSep 28, 2021v0.5.0v2.6.xAug 29, 2021v0.4.0v2.6.xAug 18, 2021DocumentationVisit thedocsfor guides,
tutorials and the API reference.IssuesIf you use this package and something does not work as you expected, pleasefile an issuedescribing your problem. We’re here to help!CreditsIf you like this software, star the repository!If you find this software useful in your research, you can cite TensorFlow MRI
using itsZenodo record.In the above link, scroll down to the “Export” section and select your favorite
export format to get an up-to-date citation.ContributionsContributions of any kind are welcome! Open an issue or pull request to begin.