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Fast Undersampled Functional Magnetic Resonance Imaging Using Nonlinear Regularized Parallel Image Reconstruction

Author

Listed:
  • Thimo Hugger
  • Benjamin Zahneisen
  • Pierre LeVan
  • Kuan Jin Lee
  • Hsu-Lei Lee
  • Maxim Zaitsev
  • Jürgen Hennig

Abstract

In this article we aim at improving the performance of whole brain functional imaging at very high temporal resolution (100 ms or less). This is achieved by utilizing a nonlinear regularized parallel image reconstruction scheme, where the penalty term of the cost function is set to the L1-norm measured in some transform domain. This type of image reconstruction has gained much attention recently due to its application in compressed sensing and has proven to yield superior spatial resolution and image quality over e.g. Tikhonov regularized image reconstruction. We demonstrate that by using nonlinear regularization it is possible to more accurately localize brain activation from highly undersampled k-space data at the expense of an increase in computation time.

Suggested Citation

  • Thimo Hugger & Benjamin Zahneisen & Pierre LeVan & Kuan Jin Lee & Hsu-Lei Lee & Maxim Zaitsev & Jürgen Hennig, 2011. "Fast Undersampled Functional Magnetic Resonance Imaging Using Nonlinear Regularized Parallel Image Reconstruction," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-9, December.
    • Handle: RePEc:plo:pone00:0028822
    DOI: 10.1371/journal.pone.0028822
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