Radial Interstices Enable Speedy Low-Volume imagING

This is a reconstruction toolbox optimised for 3D non-cartesian MR images. There are many high quality MR recon toolboxes available, e.g. BART, but these are mostly optimised for 2D sequences. 3D non-cartesian sequences present unique challenges for efficient reconstruction, so we wrote our own.

This toolbox was presented at ISMRM 2020.

Authors

Tobias C Wood, Emil Ljungberg, Florian Wiesinger.

Installation

Pre-compiled executables are provided for Linux and Mac OS X in a .tar.gz archive from http://github.com/spinicist/riesling/releases. Download the archive and extract it with tar -xzf riesling-platform.tar.gz. Then, move the resulting riesling executable to somewhere on your $PATH, for instance /usr/local/bin. That's it.

• MacOS Catalina or higher users should use curl to download the binary, i.e. curl -L https://github.com/spinicist/riesling/releases/download/v1.0/riesling-macos.tar.gz. This is because Safari now sets the quarantine attribute of all downloads, which prevents them being run as the binary is unsigned. It is possible to remove the quarantine flag with xattr, but downloading with curl is more straightforward.
• The Linux executable is compiled on Ubuntu 20.04 and a statically linked libstdc++. This means it will hopefully run on most modern Linux distributions. Let us know if it doesn't.
• The Mac executable is compiled with MacOS 14.

Usage

RIESLING comes as a single executable file with multiple commands, similar to git or bart. Type riesling to see a list of all the available commands. If you run a RIESLING command without any additional parameter RIESLING will output all available options for the given command.

RIESLING uses HDF5 (.h5) files for input and output. Your input file will need to contain the non-cartesian data, the non-cartesian trajectory, and the image geometry/orientation information. Some helper functions are provided for creating a suitable .h5 file from Python or Matlab. These are in the repository but not included as part of the installation - you will need to download and install these yourself.

Once you have assembled the input dataset, the first command you should start with is riesling recon-lsq. This will perform a least-squares reconstruction of the data using a pre-conditioned iterative algorithm. If the resulting image looks good, then the recon-rlsq command contains options for a regularized least-squares reconstruction (e.g. Total Variation or Total Generalized Variation).

To view the images, the nii command will convert from the output .h5 file format to Nifti.

A separate examples repository https://github.com/spinicist/riesling-examples contains Jupyter notebooks demonstrating most functionality.

Documentation & Help

Further documentation is available at https://riesling.readthedocs.io.

If you can't find an answer there or in the help strings, you can open an issue, or e-mail tobias.wood@kcl.ac.uk.

Compilation

If you wish to compile RIESLING yourself, compilation should hopefully be straightforward as long as you have access to a C++20 compiler (GCC 10 or higher, Clang 7 or higher). RIESLING relies on vcpkg for dependency management. To download and compile RIESLING, follow these steps:

0. MacOS Dependencies

Install the MacOS vcpkg dependencies.

1. XCode from the AppStore
2. Run $ xcode-select --install in the terminal

You may also need to install pkg-config depending on your macOS version. This is easily installed with Homebrew using

$ brew install pkg-config

Apple Silicon (M1) is supported.

0. Linux Dependencies

Install the Linux vcpkg dependencies. These include cmake, tar, curl, zip, unzip, pkg-config & build-essential. You may be surprised by which distributions do not include these by default.

1. Clone repository

$ git clone https://github.com/spinicist/riesling

2. Compile

In the riesling folder execute

$ ./bootstrap.sh