Functional MRI using regularized parallel imaging acquisition.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Lin, F-H; Huang, T-Y; Chen, N-K; Wang, F-N; Stufflebeam, SM; Belliveau, JW; Wald, LL; Kwong, KK

Published Date

  • August 2005

Published In

Volume / Issue

  • 54 / 2

Start / End Page

  • 343 - 353

PubMed ID

  • 16032694

Pubmed Central ID

  • 16032694

International Standard Serial Number (ISSN)

  • 0740-3194

Digital Object Identifier (DOI)

  • 10.1002/mrm.20555

Language

  • eng

Conference Location

  • United States