Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T

Bianciardi, Marta; Gelderen, Peter van; Duyn, Jeff H.

doi:10.1002/hbm.22320

Cited by 43 publications

(46 citation statements)

References 33 publications

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“…This effect, which arises in part from the different contrast caused by the same susceptibility distribution in phase and magnitude images, can be reduced by increasing the spatial resolution, if the signal to thermal noise ratio is sufficient (Petridou et al, 2009). In line with this, an essential aspect of the present study relative to previous studies on activation-related BOLD phase (Arja et al, 2010;Bianciardi et al, 2011;Feng et al, 2009;Hagberg et al, 2008;Hagberg et al, 2012;Menon, 2002;Petridou et al, 2009;Rowe et al, 2007;Tomasi and Caparelli, 2007) and BOLD susceptibility changes in humans Bilgic et al, 2013;Chen et al, 2013), is the increased spatial resolution (1 mm isotropic) and the deliberate avoidance of spatial smoothing. Both the number of activated voxels and the number of common voxels increased significantly when the fMRI data were spatially smoothed before the GLM fit (Balla et al, 2012).…”

Section: Noise Sensitivity and Spatial Reliability Of Fqsmsupporting

confidence: 55%

“…We will refer to this filter combination as DORK with SHARP. The three other alternative spatio-temporal filters were: (ii) complex regression of global phase changes in image space (NVR, ); (iii) 2D Gaussian homodyne high-pass filtering of the unwrapped phase with a filter width of 6 mm (homodyne, (Deistung et al, 2008;Haacke et al, 2004;Noll et al, 1991)) and (iv) removal of static phase components by complex division (Tomasi and Caparelli, 2007) in combination with 8 th order 2D polynomial high-pass filtering of the resulting relative phase images (RELPOLY, (Bianciardi et al, 2011)). The time-series from the multiple fMRI runs were co-registered using the FLIRT tool (Jenkinson et al, 2002) by aligning the motion correction reference volumes (i.e.…”

Section: Preprocessing Pipeline Of the Time-seriesmentioning

confidence: 99%

“…Furthermore, in a previous study Bianciardi et al (2013) compared RETROICOR to a novel filter combination broadly matching one composed of NVR and RELPOLY, and found that their filter combination was more efficient in removing physiological noise. Hagberg et al (2012) comprehensively analyzed and compared several physiological noise filters for complex time-series and also found that homodyne filtering, NVR and a modified version of DORK outperformed RETROICOR.…”

Section: Alternative Fqsm Pipelinesmentioning

confidence: 99%

“…Only a few studies have investigated BOLD contrast by also using the phase information in time-series (Arja et al, 2010;Bianciardi et al, 2013;Chen et al, 2013;Hagberg et al, 2008;Hagberg et al, 2012;Hahn et al, 2009;Menon, 2002;Petridou et al, 2009;Rowe, 2005;Rowe and Logan, 2004;Rowe et al, 2007;Tomasi and Caparelli, 2007). At the spatial resolution allowed by conventional magnetic fields (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Such conditions are usually fulfilled for MRI acquisitions at short echo time, low field strength and low spatial resolution, and this approach has been applied to experimental data from a single subject to demonstrate the feasibility of the method. Bianciardi et al (2013) generated QSM for each volume in the phase time-series of 2.5 mm isotropic fMRI data acquired at 7 T in order to compute activation related susceptibility change maps. They showed that functional, task related magnitude and phase changes can be detected with comparable sensitivity and that these changes have the same BOLD origins.…”

Section: Introductionmentioning

confidence: 99%

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Functional quantitative susceptibility mapping (fQSM)

et al. 2014

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Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful technique, typically based on the statistical analysis of the magnitude component of the complex time-series. Here, we additionally interrogated the phase data of the fMRI time-series and used quantitative susceptibility mapping (QSM) in order to investigate the potential of functional QSM (fQSM) relative to standard magnitude BOLD fMRI. High spatial resolution data (1 mm isotropic) were acquired every 3 seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B0. Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM time-series and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining "activated" voxels in SPMs, and on the efficiency of spatiotemporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data. Our results present the potential of fQSM as a quantitative method of mapping BOLD changes. We also critically discuss the technical challenges and issues linked to this intriguing new technique. seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B 0 . Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM timeseries and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining "activated" voxels in SPMs, and on the efficiency of spatio-temporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data.

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Section: Noise Sensitivity and Spatial Reliability Of Fqsmsupporting

confidence: 55%

Section: Preprocessing Pipeline Of the Time-seriesmentioning

confidence: 99%

Section: Alternative Fqsm Pipelinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Functional quantitative susceptibility mapping (fQSM)

et al. 2014

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Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain

Liu

Wei

Tong

et al. 2014

J. Magn. Reson. Imaging

427

422

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Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique that enhances image contrast by using the susceptibility differences between tissues. It is created by combining both magnitude and phase in the gradient echo data. SWI is sensitive to both paramagnetic and diamagnetic substances which generate different phase shift in MRI data. SWI images can be displayed as a minimum intensity projection that provides high resolution delineation of the cerebral venous architecture, a feature that is not available in other MRI techniques. As such, SWI has been widely applied to diagnose various venous abnormalities. SWI is especially sensitive to deoxygenated blood and intracranial mineral deposition and, for that reason, has been applied to image various pathologies including intracranial hemorrhage, traumatic brain injury, stroke, neoplasm, and multiple sclerosis. SWI, however, does not provide quantitative measures of magnetic susceptibility. This limitation is currently being addressed with the development of quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI). While QSM treats susceptibility as isotropic, STI treats susceptibility as generally anisotropic characterized by a tensor quantity. This article reviews the basic principles of SWI, its clinical and research applications, the mechanisms governing brain susceptibility properties, and its practical implementation, with a focus on brain imaging.

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Fast quantitative susceptibility mapping with L1‐regularization and automatic parameter selection

Bilgic̦

Fan

Polimeni

et al. 2013

Magnetic Resonance in Med

Self Cite

113

137

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Purpose To enable fast reconstruction of quantitative susceptibility maps with Total Variation penalty and automatic regularization parameter selection. Methods ℓ1-regularized susceptibility mapping is accelerated by variable-splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and FFTs. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization. Results Compared to the nonlinear Conjugate Gradient (CG) solver, the proposed method offers 20× speed-up in reconstruction time. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering and ℓ1-regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 minutes using Matlab on a standard workstation compared to 22 minutes using the Conjugate Gradient solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 minutes, which would have taken 4 hours with the CG algorithm. Proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5× faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional BOLD susceptibility mapping, where processing of the massive time-series dataset would otherwise be prohibitive with the CG solver. Conclusion Online reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion.

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Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T

Cited by 43 publications

References 33 publications

Functional quantitative susceptibility mapping (fQSM)

Functional quantitative susceptibility mapping (fQSM)

Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain

Fast quantitative susceptibility mapping with L1‐regularization and automatic parameter selection

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