OCTOPUS: Off-resonance CorrecTion OPen-soUrce Software

OCTOPUS is an open-source tool that provides off-resonance correction methods for Magnetic Resonance (MR) images. In particular, the implemented techniques are Conjugate Phase Reconstruction (CPR)[1], frequency-segmented CPR [2] and Multi-Frequency Interpolation (MFI) [3].

Off-resonance is a type of MR image artifact. It originates as an accumulation of phase from off-resonant spins along the read-out direction due to field inhomogeneities, tissue susceptibilities and chemical shift among other possible sources [4]. Long read-out k-space trajectories are therefore more prone to this artifact and its consequences on the image. The image effects are tipycally blurring and/or geometrical distortion, and consequently, quality deterioration [5].

OCTOPUS leverages existing techniques and outputs artifact-corrected or mitigated image reconstruction given the raw data from the scanner, k-space trajectory and field map. It is targeted to MR scientists, researchers, engineers and students who work with off-resonance-prone trajectories, such as spirals.

To learn more about the used methods and their implementation visit the API docs.

Installation

1. Install Python (>=Python 3.6)
2. Create and activate a virtual environment (optional but recommended)
3. Copy and paste this command in your terminal pip install MR-OCTOPUS

Otherwise, skip the installation! Run OCTOPUS in your browser instead.

Quick start

The Examples folder contains scripts and data to run off-resonance correction on numerical simulations and phantom images for different k-space trajectories and field maps.

After the installation is completed, download the example data. Now you can run two types of demos. More information about these and other experiments can be found in the Wiki page.

1. Numerical simulations

numsim_cartesian.py and numsim_spiral.py run a forward model on a 192x192 Shepp-Logan phantom image. They simulate the off-resonance effect of a cartesian and spiral k-space trajectory, respectively, given a simulated field map.

With OCTOPUS.fieldmap.simulate you can experiment the effect of the type of field map and its frequency range on the output corrupted image.

The corrupted image is then corrected using CPR, fs-CPR and MFI and the results are displayed.

2. In vitro experiment

If you want to use OCTOPUS to correct real data, you can use ORC_main.py as a template. 1. Fill the settings.ini file with the paths for your inputs and outputs. NOTE: the default settings are configured to run the script using the sample data provided. 2. Input your field of view (FOV), gradient raster time (dt), and echo time (TE). python FOV = # meters dt = # seconds TE = # seconds 3. Check that the dimensions of your inputs agree. rawdata dims = ktraj dims 5. Specify the number of frequency segments for the fs-CPR and MFI methods python Lx = # L=Lmin * Lx 6. Run the script. The program will display an image panel with the original image and the corrected versions.

3. Command line implementation - NEW!

Now you can easily run OCTOPUS using commands on your terminal. After installation, type: python OCTOPUS_cli path/to/container/folder rawdata_file kspace_trajectory_file field_map_file correction_method For more information about the command line implemetation and its required arguments, type: python OCTOPUS_cli -h For more information about how to structure your data, visit the API docs.

Skip the installation! - OCTOPUS in your browser

There's no need to go through the installation process. Using this template you can now run off-resonance correction in your browser!

As a demo, you can use the example data provided for the in vitro experiment.

Contributing and Community guidelines

OCTOPUS adheres to a code of conduct adapted from the Contributor Covenant code of conduct. Contributing guidelines can be found here.

References

1. Maeda, A., Sano, K. and Yokoyama, T. (1988), Reconstruction by weighted correlation for MRI with time-varying gradients. IEEE Transactions on Medical Imaging, 7(1): 26-31. doi: 10.1109/42.3926
2. Noll, D. C., Pauly, J. M., Meyer, C. H., Nishimura, D. G. and Macovskj, A. (1992), Deblurring for non‐2D fourier transform magnetic resonance imaging. Magn. Reson. Med., 25: 319-333. doi:10.1002/mrm.1910250210
3. Man, L., Pauly, J. M. and Macovski, A. (1997), Multifrequency interpolation for fast off‐resonance correction. Magn. Reson. Med., 37: 785-792. doi:10.1002/mrm.1910370523
4. Noll, D. C., Meyer, C. H., Pauly, J. M., Nishimura, D. G. and Macovski, A. (1991), A homogeneity correction method for magnetic resonance imaging with time-varying gradients. IEEE Transactions on Medical Imaging, 10(4): 629-637. doi: 10.1109/42.108599
5. Schomberg, H. (1999), Off-resonance correction of MR images. IEEE Transactions on Medical Imaging, 18( 6): 481-495. doi: 10.1109/42.781014