Quick Links: Home, Download & Installation, Tutorials, List of Features, References & Reproducibility

**Figure:** Simulated MRI images.

1. Linux/unix terminal

2. Python

3. Matlab

1. Non-Cartesian SENSE reconstruction

2. Non-Cartesian SENSE reconstruction

1. Subspace-constrained reconstruction

2. Model based reconstruction

1. Introduction to dynamic MRI reconstruction with BART

2. Dynamic Contrast Enhanced (DCE) MRI

1. Introduction to machine learning reconstruction with BART

2. Machine learning reconstruction with BART

3. TensorFlow and deep learning reconstruction with BART

The BART solution of the SENSE reproducibility challenge

The 2020-2022 webinar series

This two-days webinar (from June 2020) introduced the concepts of working with BART through the linux/unix command line.

- Where to find docs, examples, and help
- Discussion of file format and dimensions
- Working with Command Line Interface (CLI) tools and Matlab/Python wrappers
- Data preprocessing
- Compressed Sensing and non-Cartesian MRI reconstruction
- GRASP-like MRI reconstruction

- Intro to the C-programming library
- Set up a build environment, compile, and run
- Add command-line parameter to existing tool
- Build a basic tool and system test

- The basic structure of BART commands
- displaying images
- using the BART phantom tool
- viewing data dimensions
- Bitmasks
- Low-pass filtering
- Reconstruction of multi-coil data
- Reconstruction with parallel imaging compressed sensing (PICS)

- Creating multi-channel k-space data
- Using BART's Shepp Logan phantom
- Coil compression and image reconstruction
- Generating ESPIRIT sensitivity maps using BART's ecalib tool
- Using different regularization techniques

The webinar we had on July 2021 includes a demo and hands-on exercise for working with BART in a python environment. It covers:

- How to create phantom data and subsampling masks in BART.
- How to use BART for parallel MRI reconstruction.

The ESMRMB MRI-Together 2021 workshop included a demo and hands-on exercise for working with BART in a python environment. It covers:

- How to set up an environment for BART.
- The BART command structure and data format.
- How to use Bitmasks to select dimensions.
- How to create a Cartesian k-space phantom.
- Example: Subspace T1 Mapping - including coil compression, trajectory generation and coil sensitivity estimation.
- Introduction to using BART for machine learning, including reconstruction with MoDL and variational networks.

- Loading data from h5 files
- Noise whitening
- Visualizing the sampling pattern
- Reconstruction
- Computing g-factor for parallel imaging.

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 webinar of March 2021 included demos and hands-on tutorials for:

- Subspace-constrained reconstructions
- Analytical model-based phantom simulation

The ISMRM 2021 BART software tutorial included an interactive demo on nonlinear model-based reconstruction for quantitative MRI (T1 mapping, water-fat separation)

The webinar of Dec 2020 included demos and hands-on tutorials for:

- Dynamic (temporal sequence) MRI data loading, dimensions organization, and sensitivity maps computation.
- Image reconstruction using parallel-imaging-compressed-sensing (PICS), with advanced regularization methods suitable for dynamic MRI data.

The ISMRM 2016 software demo included a tutorial on dynamic axial-slice reconstruction with BART.

BART webinar #6 (March 1, 2022) included demos and hands-on tutorials for working with BART in a python environment. It covers:

- Introduction to using BART for machine learning.
- TensorFlow-regularization + BART Reconstruction.
- Examples for data pre-processing.
- Examples for training MoDL and a Variational Network with BART.

The ESMRMB MRI-Together 2021 workshop included a demo and hands-on exercise for working with BART in a python environment. It covers:

- Introduction to using BART for machine learning.
- Reconstruction with MoDL and Variational Networks.

The ISMRM 2021 BART tutorial included demos and hands-on tutorials for:

- TensorFlow-Regularizer + BART Reconstruction
- Neural networks with BART
- How to run BART on Google Collab

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

**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