Figure: l1-ESPIRiT reconstruction of a human abdomen (variable-density Poisson-disc
sampling, R=7, RF-spoiled 3D-FLASH, B0 = 3T TR/TE = 4.3/1.0ms, partial echo .6, matrix: 320x256x184, 32 channels)
Total reconstruction time: 51s including compression (14s), calibration (9s), iterative reconstruction (12s), and other processing steps (16s) on a multi-GPU system.
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 which is currently in development can be found here.
Please direct all questions or comments to the public mailing list:
(or contact the main author: martin.uecker at med.uni-goettingen.de)
Note: The software is intended for research use only and NOT FOR DIAGNOSTIC USE. It comes without any warranty (see LICENSE for details).
It is recommended to download the latest release. The latest release can always be found here.
Attention: Windows users should still use version v0.3.01 because a library is missing on cygwin which is requited to compile later versions.
BART has also been included in Debian GNU/Linux. The Debian binary package can be reproducibly built from the source code (as distributed by Debian) and can be downloaded from here. The package should also work on Ubuntu although this is not guaranteed.
For developers: the C source code can be found in the GitHub repository
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
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.
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.
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
A a generic reference (all versions): BART Toolbox for Computational Magnetic Resonance Imaging, DOI: 10.5281/zenodo.592960