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Neuroinformatics

, Volume 11, Issue 3, pp 291–300 | Cite as

A Graphics Processing Unit Accelerated Motion Correction Algorithm and Modular System for Real-time fMRI

  • Dustin Scheinost
  • Michelle Hampson
  • Maolin Qiu
  • Jitendra Bhawnani
  • R. Todd Constable
  • Xenophon Papademetris
Original Article

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project (www.bioimagesuite.org). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences.

Keywords

Real-time fMRI Motion correction Graphics processing unit Open-source software 

Notes

Acknowledgments

We thank J. Brewer and P. Worhnsky for the development of the front-end used for the current meditation study and for the example feedback shown in Fig. 3a. We also thank E. Finn for her helpful comments on the manuscript. This study was funded by the Dana foundation (M. Hampson) and NIH (R01 EB006494, R03 EB012969, RO1 EB009666, R01 NS051622, R21 MH090384).

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Dustin Scheinost
    • 1
    • 4
  • Michelle Hampson
    • 2
  • Maolin Qiu
    • 2
  • Jitendra Bhawnani
    • 2
  • R. Todd Constable
    • 1
    • 2
    • 3
  • Xenophon Papademetris
    • 1
    • 2
  1. 1.Department of Biomedical EngineeringYale UniversityNew HavenUSA
  2. 2.Department of Diagnostic RadiologyYale UniversityNew HavenUSA
  3. 3.Department of NeurosurgeryYale UniversityNew HavenUSA
  4. 4.Magnetic Resonance Research CenterNew HavenUSA

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