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MAGMA. 2012 Aug;25(4):313-20. doi: 10.1007/s10334-011-0290-7. Epub 2011 Nov 16.

A highly parallelized framework for computationally intensive MR data analysis.

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1
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Abstract

OBJECT:

The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation.

MATERIALS AND METHODS:

Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework.

RESULTS:

A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15.

CONCLUSION:

The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.

PMID:
22086306
DOI:
10.1007/s10334-011-0290-7
[Indexed for MEDLINE]
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