Functional MRI using regularized parallel imaging acquisition

Fa Hsuan Lin, Teng Yi Huang, Nan Kuei Chen, Fu Nien Wang, Steven M. Stufflebeam, John W. Belliveau, Lawrence L. Wald, Kenneth K. Kwong

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.

Original languageEnglish (US)
Pages (from-to)343-353
Number of pages11
JournalMagnetic Resonance in Medicine
Volume54
Issue number2
DOIs
StatePublished - Aug 2005
Externally publishedYes

Keywords

  • Brain
  • EPI
  • Parallel MRI
  • SENSE
  • fMRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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