Distributed MRI reconstruction using gadgetron‐based cloud computing. Issue 3 (31st March 2014)

• Record Type: Journal Article
• Authors: Xue, Hui; Inati, Souheil; Sørensen, Thomas Sangild; Kellman, Peter; Hansen, Michael S.
• Is Part Of: Magnetic resonance in medicine. Volume 73:Issue 3(2015:Mar.)
• Page Start: 1015
• Page End: 1025
• Publication Date: 2014-03-31
• Subjects: Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.25213
• Languages: English
• ISSNs: 0740-3194

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="mrm25213-sec-0001" sec-type="section"> <title>Purpose</title> <p>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.</p> </sec> <sec id="mrm25213-sec-0002" sec-type="section"> <title>Methods</title> <p>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).</p> </sec> <sec id="mrm25213-sec-0003" sec-type="section"> <title>Results</title> <p>The proposed setup was able to handle acquisition and <italic>1</italic><sub>1</sub>‐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 mm<sup>3</sup> isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min.</p> </sec> <sec id="mrm25213-sec-0004" sec-type="section"> <title>Conclusion</title> <p>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. <bold>Magn Reson Med 73:1015–1025, 2015. © 2014 Wiley Periodicals, Inc.</bold></p> </sec> </abstract>