NDLI: GPU-based motion correction of contrast-enhanced liver MRI scans: An OpenCL implementation

Content Provider	IEEE Xplore Digital Library
Author	Jihun Oh Martin, D. Skrinjar, O.
Copyright Year	2011
Description	Author affiliation: Department of Radiology, Emory University School of Medicine, Atlanta, GA 30322, USA (Martin, D.) \|\| School of Electrical and Computer Engineering, Georgia Tech, Atlanta, 30332, USA (Jihun Oh; Skrinjar, O.)
Abstract	Clinical diagnosis and quantification of liver disease have been improved through the development of techniques using contrast-enhanced liver MRI sequences. To qualitatively or quantitatively analyze such image sequences, one first needs to correct for rigid and non-rigid motion of the liver. For motion correction of the liver, we have employed bi-directional local correlation coefficient Demons, which is a variation of the original Demons method. However, despite the intrinsic speed of the Demons method, the run-time on the order of an hour of its CPU-based implementation is not sufficiently short for a regular clinical use. For this reason we implemented the method on a graphics processing unit (GPU) using OpenCL. On NVIDIA GTX 260M, which is a laptop GPU, we achieved sub-minute runtime for the motion correction of typical liver MRI scans, which was ∼50 times faster than its CPU-based implementation. A sub-minute runtime of liver MRI motion correction allows for its regular clinical use.
Starting Page	783
Ending Page	786
File Size	237529
Page Count	4
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872522
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing unit Magnetic resonance imaging Image registration Hardware Kernel Liver diseases Demons graphics processing unit GPU OpenCL contrast-enhanced liver MRI image registration
Content Type	Text
Resource Type	Article

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