Material for BART demo at the Sedona 2016 workshop
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README.md

README.md

BART Workshop Materials

This repository contains information and demos for the Berkeley Advanced Reconstruction Toolbox (BART). This material was presented at the 2016 ISMRM Workshop on Data Sampling & Image Reconstruction.

Purpose

The purpose of this repository is to host and share demos and workshop materials for BART. From the website:

The Berkeley Advanced Reconstruction Toolbox (BART) is a free and open-source image-reconstruction framework for Computational Magnetic Resonance Imaging. 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.

Getting Started

The most up-to-date information can be found at the official BART website: http://mrirecon.github.io/bart.

The workshop material was tested with BART version 0.3.01

Download

The source code is available at https://github.com/mrirecon/bart/archive/v0.3.01.tar.gz. Untar and navigate to the bart directory:

wget https://github.com/mrirecon/bart/archive/v0.3.01.tar.gz
tar -xvvf v0.3.01.tar.gz && mv bart-0.3.01 bart
cd bart

Quick Installation

See the Quick-Install guide for quick installation instructions.

Demos

The demos directory contains standalone demos that show different BART use cases. The demos are self-documented within each directory, and are summarized below.

  1. Simulate phantom data and compare regularized reconstructions (pics-phantom)
  2. Reconstruct an axial slice of dynamic contrast enhanced (DCE) data (pics-dce)
  3. Build a GRASP reconstruction tool with bash scripting and BART command-line tools (grasp)
  4. Build a Wave-CS reconstruction tool in C using the BART C API (wave)
  5. Simulate multi-channel data and computer g-factor using Python and BART(gfactor)