Functional MRI using regularized parallel imaging acquisition

Brigham and Women's Hospital, Boston, Massachusetts, United States
Magnetic Resonance in Medicine (Impact Factor: 3.57). 08/2005; 54(2):343-53. DOI: 10.1002/mrm.20555
Source: PubMed


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
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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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    ABSTRACT: The acquisition time of BOLD contrast functional MRI (fMRI) data with whole-brain coverage typically requires a sampling rate of one volume in 1-3seconds. Although the volumetric sampling time of a few seconds is adequate for measuring the sluggish hemodynamic response (HDR) to neuronal activation, faster sampling of fMRI might allow for monitoring of rapid physiological fluctuations and detection of subtle neuronal activation timing information embedded in BOLD signals. Previous studies utilizing a highly accelerated volumetric MR inverse imaging (InI) technique have provided a sampling rate of one volume per 100ms with 5mm spatial resolution. Here, we propose a novel modification of this technique, the echo-shifted InI, which allows TE to be longer than TR, to measure BOLD fMRI at an even faster sampling rate of one volume per 25ms with whole-brain coverage. Compared with conventional EPI, echo-shifted InI provided 80-fold speedup with similar spatial resolution and less than 2-fold temporal SNR loss. The capability of echo-shifted InI to detect HDR timing differences was tested empirically. At the group level (n=6), echo-spaced InI was able to detect statistically significant HDR timing differences of as low as 50ms in visual stimulus presentation. At the level of individual subjects, significant differences in HDR timing were detected for 400ms stimulus-onset differences. Our results also show that the temporal resolution of 25ms is necessary for maintaining the temporal detecting capability at this level. With the capabilities of being able to distinguish the timing differences in the millisecond scale, echo-shifted InI could be a useful fMRI tool for obtaining temporal information at a time scale closer to that of neuronal dynamics.
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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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    ABSTRACT: Although functional imaging of neuronal activity by magnetic resonance imaging (fMRI) has become the primary methodology employed in studying the brain, significant portions of the brain are inaccessible by this methodology due to its sensitivity to macroscopic magnetic field inhomogeneities induced near air-filled cavities in the head. In this paper, we demonstrate that this sensitivity is eliminated by a novel pulse sequence, RASER (rapid acquisition by sequential excitation and refocusing) (Chamberlain et al., 2007), that can generate functional maps. This is accomplished because RASER acquired signals are purely and perfectly T(2) weighted, without any T(2)*-effects that are inherent in the other image acquisition schemes employed to date. T(2)-weighted fMRI sequences are also more specific to the site of neuronal activity at ultrahigh magnetic fields than T(2)*-variations since they are dominated by signal components originating from the tissue in the capillary bed. The RASER based fMRI response is quantified; it is shown to have an inherently less noisy time series and to provide fMRI in brain regions, such as the orbitofrontal cortex, which are challenging to image with conventional techniques.
    Full-text · Article · Jan 2011 · NeuroImage
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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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    ABSTRACT: In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.
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