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A highly parallelized framework for computationally intensive MR data analysis

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

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.

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Acknowledgments

This study was funded by the Institute for the Study of Affective Neuroscience (ISAN), by the Oesterreichische Nationalbank (OeNB P13890, OeNB P13903, OeNB P12982) and by the Austrian Science Fund (FWF) as part of the Special Research Program 35 (SFB-35). The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC).

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Authors and Affiliations

  1. Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria

    Roland N. Boubela, Wolfgang Huf, Klaudius Kalcher, Ronald Sladky, Christian Windischberger & Ewald Moser

  2. Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria

    Roland N. Boubela, Wolfgang Huf, Klaudius Kalcher, Lukas Pezawas & Siegfried Kasper

  3. Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstraße 8-10, 1040, Vienna, Austria

    Roland N. Boubela, Wolfgang Huf, Klaudius Kalcher & Peter Filzmoser

Authors
  1. Roland N. Boubela
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  2. Wolfgang Huf
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  3. Klaudius Kalcher
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  4. Ronald Sladky
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  7. Siegfried Kasper
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  8. Christian Windischberger
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  9. Ewald Moser
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Corresponding author

Correspondence to Christian Windischberger.

Additional information

Roland N. Boubela and Wolfgang Huf contributed equally to this paper.

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Boubela, R.N., Huf, W., Kalcher, K. et al. A highly parallelized framework for computationally intensive MR data analysis. Magn Reson Mater Phy 25, 313–320 (2012). https://doi.org/10.1007/s10334-011-0290-7

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  • DOI: https://doi.org/10.1007/s10334-011-0290-7

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