Distributed MRI reconstruction using gadgetron-based cloud computing
Article first published online: 31 MAR 2014
© 2014 Wiley Periodicals, Inc.
Magnetic Resonance in Medicine
Volume 73, Issue 3, pages 1015–1025, March 2015
How to Cite
Xue, H., Inati, S., Sørensen, T. S., Kellman, P. and Hansen, M. S. (2015), Distributed MRI reconstruction using gadgetron-based cloud computing. Magn Reson Med, 73: 1015–1025. doi: 10.1002/mrm.25213
- Issue published online: 18 FEB 2015
- Article first published online: 31 MAR 2014
- Manuscript Accepted: 18 FEB 2014
- Manuscript Revised: 28 JAN 2014
- Manuscript Received: 11 DEC 2013
- National Heart, Lung and Blood Institute Intramural Research Program
- distributed computing;
- nonlinear MRI reconstruction;
- open-source software
To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency.
The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications).
The proposed setup was able to handle acquisition and 11-SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm3 isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min.
A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency. Magn Reson Med 73:1015–1025, 2015. © 2014 Wiley Periodicals, Inc.