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Methods Inf Med 2005; 44(05): 665-673
DOI: 10.1055/s-0038-1634023
Original Article
Schattauer GmbH

Enabling On-demand Real-time Functional MRI Analysis Using Grid Technology

E. Bagarinao
1   Photonics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Kansai Center, Ikeda City, Osaka, Japan
2   Grid Technology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba City, Ibaraki, Japan
,
K. Matsuo
1   Photonics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Kansai Center, Ikeda City, Osaka, Japan
,
Y. Tanaka
2   Grid Technology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba City, Ibaraki, Japan
,
L. F. G. Sarmenta
3   Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Metro Manila, Philippines
,
T. Nakai
1   Photonics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Kansai Center, Ikeda City, Osaka, Japan
› Author Affiliations
Further Information

Publication History

Received: 28 May 2004

accepted: 05 January 2005

Publication Date:
07 February 2018 (online)

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Summary

Objectives: The analysis of brain imaging data such as functional MRI often requires considerable computing resources, which in most cases are not readily available in many medical imaging facilities. This lack of computing power makes it difficult for researchers and medical practitioners alike to perform on-site analysis of the generated data. This paper presents a system that is capable of analyzing functional MRI data in real time with results available within seconds after data acquisition.

Methods: The system employs remote computational servers to provide the necessary computing power. System integration is accomplished by an accompanying software package, which includes fMRI analysis tools, data transfer routines, and an easy-to-use graphical user interface. The remote analysis is transparent to the user as if all computations are performed locally.

Results: The use of PC clusters in the analysis of fMRI data significantly improved the performance of the system. Simulation runs fully achieved real-time performance with a total processing time of 1.089 s per image volume (64 x 64 x 30 in size), much less than the per volume acquisition time set to 3.0 s.

Conclusions: The results show the feasibility of using remote computational resources to enable on-demand real-time fMRI capabilities to imaging sites. It also offers the possibility of doing more intensive analysis even if the imaging site doesn’t have the necessary computing resources.

Keywords

On-demand real-time fMRI analysis - computational grids - incremental GLM analysis
 

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