Article

Functional MRI using regularized parallel imaging acquisition

Massachusetts General Hospital, Department of Radiology, MGH-HMS-MIT, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129, USA.
Magnetic Resonance in Medicine (Impact Factor: 3.4). 08/2005; 54(2):343-53. DOI: 10.1002/mrm.20555
Source: PubMed

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.

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Available from: Fa-Hsuan Lin, Jul 31, 2015
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    • "In fMRI, parallel MRI has been successfully combined with the gradient-echo EPI accelerated acquisitions (Preibisch et al., 2003; Schmidt et al., 2005). It has also been demonstrated that incorporating a static image as prior information can further improve the sensitivity of fMRI (Lin et al., 2005). Other multi-slice-based approaches such as echo-shifted multislice EPI (Gibson et al., 2006), Multiplexed-EPI (M-EPI) (Feinberg et al., 2010) and blipped-CAIPI (Setsompop et al., 2012) can acquire one brain volume in 250–400 ms. "
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    • "Use of passive shims does not fully eliminate magnetic field variations (Cusack et al., 2005, Juchem et al., 2006, Koch et al., 2006, Wilson et al., 2003, Wilson and Jezzard, 2003, Yang et al., 2006). Parallel imaging alleviates the problem, but does not eliminate it completely (Bellgowan et al., 2006, de Zwart et al., 2006, Golay et al., 2004, Lin et al., 2005, Moeller et al., 2006, Preibisch et al., 2003, Pruessmann et al., 1999). "
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    • "T2 Ã -weighted echo-planar imaging (EPI) used in fMRI typically suffers from signal distortion or drop-out in certain areas due to magnetic field inhomogeneities. Recent work in parallel imaging acquisition promises to reduce such artifacts (Lin et al., 2005). Moreover, artifact correction and EPI unwarping based on field maps (Jezzard and Balaban, 1995) improve the quality of the fMRI alignment with anatomical MRI. "
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