Reconstruction code for fast and flexible free-breathing dynamic volumetric MRI.

We are making available joint compressed sensing and parallel imaging reconstruction of golden-angle radial MRI data with arbitrary temporal resolution.

The MATLAB code includes the core reconstruction algorithm and examples of dynamic contrast-enhanced MRI of the liver.

Golden-angle radial sparse parallel (GRASP) MRI combines parallel imaging and compressed sensing, outperforming either technique. GRASP delivers high spacial and temporal resolution in volumetric MRI and is highly robust to the effects of patient motion. These characteristics make GRASP especially valuable in dynamic contrast-enhanced MRI of patients who may have trouble remaining still for long periods or performing lengthy breath holds in the scanner.

GRASP has been translated into clinical use at NYU Langone Health and many medical centers around the world. The technique is available on Siemens MRI scanners under the name Compressed Sensing GRASP-VIBE.

Related Publication

Feng L, Grimm R, Tobias Block K, Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R.
Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI
Magn Reson Med. 2014 Sep;72(3):707-17. doi: 10.1002/mrm.24980

Please cite this work if you are using GRASP in your research.

Get the Code

The software available on this page is provided free of charge and comes without any warranty. CAI²R and NYU Grossman School of Medicine do not take any liability for problems or damage of any kind resulting from the use of the files provided. Operation of the software is solely at the user’s own risk. The software developments provided are not medical products and must not be used for making diagnostic decisions.

The software is provided for non-commercial, academic use only. Usage or distribution of the software for commercial purpose is prohibited. All rights belong to the authors (Li Feng, Ricardo Otazo) and NYU Grossman School of Medicine. If you use the software for academic work, please give credit to the author in publications and cite the related publications.

Please spell out your affiliation (e.g. “New York University” rather than “NYU”).


  • Li Feng
  • Ricardo Otazo


Questions about this resource may be directed to Li Feng, PhD or Ricardo Otazo, PhD.

Related Resources

Related Post