Toolbox for Computational Magnetic Resonance Imaging

The Berkeley Advanced Reconstruction Toolbox (BART) toolbox is a free and open-source image-reconstruction framework for Computational Magnetic Resonance Imaging developed by the research groups of Martin Uecker (Graz University of Technology), Jon Tamir (UT Austin), and Michael Lustig (UC Berkeley). It consists of a programming library and a toolbox of command-line programs. The library provides common operations on multi-dimensional arrays, Fourier and wavelet transforms, as well as generic implementations of iterative optimization algorithms. The command-line tools provide direct access to basic operations on multi-dimensional arrays as well as efficient implementations of many calibration and reconstruction algorithms for parallel imaging and compressed sensing.

bart logo reconstructed from k-space

Figure: Simulated MRI images.

List of Features

  • basic features:
    • runs on Linux and Mac OS X
    • multi-dimensional operations on arrays
    • fast non-uniform Fourier Transform (nuFFT and convolution-based method)
    • multi-dimensional (divergence-free) wavelet transform
    • parallel computation on multiple cores and with Graphical Processing Units (GPU)
  • iterative methods:
    • Conjugate Gradients (CG)
    • (Fast) Iterative Soft-Thresholding Algorithm (ISTA and FISTA)
    • Normalized iterative hard thresholding (NIHT)
    • Alternating Direction Method of Multipliers (ADMM)
    • Iteratively Regularized Gauss-Newton Method (IRGNM)
    • Chambolle-Pock primal-dual algorithm
  • calibration methods:
    • direct calibration of coil sensitivities from k-space center
    • Walsh's method for calibration of coil sensitivities
    • ESPIRiT
    • (geometric) channel compression and whitening
    • RING: estimation of gradient delays for radial MRI
  • reconstruction methods for MRI:
    • iterative parallel imaging reconstruction: POCSENSE, SENSE
    • compressed sensing and parallel imaging
    • calibration-less parallel imaging: NLINV and ENLIVE (non-linear optimization) and SAKE (structured low-rank matrix completion)
    • reconstruction with linear subspace constraints
    • non-linear model-based reconstruction for T1, T2, flow, and water-fat mapping
    • methods based on deep learning: variational networks and MoDL
  • regularization (in arbitrary dimensions):
    • Tikhonov
    • total variation
    • l1-wavelet
    • (multi-scale) low-rank
    • Tensorflow-based priors

Example

Perform ESPIRiT calibration and image reconstruction with l1-wavelet regularization:

 $ bart ecalib kspace sensitivities
 $ bart pics -l1 -r0.001 kspace sensitivities image_out

A python-based image viewer (bartview.py) which can read the BART data format is included in the source repository.

An image viewer for Linux and Mac OS X can be found here.

You can try BART directly in your browser: binder

Mailing List

Please direct all questions or comments to the public mailing list:

mrirecon list (public)

(or contact the main author: martin.uecker at med.uni-goettingen.de)

Download

Note: The software is intended for research use only and NOT FOR DIAGNOSTIC USE. It comes without any warranty (see LICENSE for details).

Releases:

It is recommended to download the latest release. All releases can be found here.

Selected releases:

BART has also been included in Debian GNU/Linux (and Ubuntu). The Debian binary package can be reproducibly built from the source code (as distributed by Debian) and can be downloaded from here. There is also a package for the image viewer. We also provide unofficial packages for Fedora and CentOS: .

Please note that running BART on Windows is not supported. Nevertheless, some versions of BART are reported to work on Windows using WSL or Cygwin.

For developers: the C source code can be found in the GitHub repository

Installation

Installation of the required libraries, downloading and unpacking of the archive, and compilation on Linux is usually as simple as typing the following commands:

 $ sudo apt-get install make gcc libfftw3-dev liblapacke-dev libpng-dev libopenblas-dev
 $ wget https://github.com/mrirecon/bart/archive/vX.Y.ZZ.tar.gz
 $ tar xzvf vX.YY.ZZ.tar.gz
 $ cd bart-X.YY.ZZ
 $ make

See the README file included with the source code for further instructions and for Mac OS X and Windows.

If you are a Docker user you can also start with this extremely simple Dockerfile.

Matlab Interface and Examples

The toolbox can also be used in combination with Matlab/Octave.

 >> sensitivities = bart('ecalib', kspace);
 >> image_out = bart('pics -l1 -r0.001', kspace, sensitivities);

More examples where the tools are called directly from Matlab can be found here.

Matlab code and data: GitHub repository

A Matlab-based image viewer which works well with BART is arrayShow by Tilman Sumpf.

Webinar and Workshop Materials with Examples

Webinars

There are new tutorials from our Webinar which you can find in a GitHub repository.

Workshops

ISMRM 2021 As part of the ISMRM 2021 meeting, we gave a demo of the new features of BART related to non-linear model-based reconstruction and deep learning integration.

ISMRM 2016 The toolbox was presented at the ISMRM 2016 Data Sampling and Image Reconstruction Workshop. This material was created for BART version 0.3.00 and later versions might have minor differences. Please check the README included with each release for up-to-date installation instructions.

Demo code and data: GitHub repository

Reproducible Research

This is a list of research paper which can be reproduced using BART.

References

A a generic reference (all versions): BART Toolbox for Computational Magnetic Resonance Imaging, DOI: 10.5281/zenodo.592960