Elimination of k-space spikes in fMRI data

Zhang, Xiaodong; Moortele, Pierre-François Van de; Pfeuffer, Josef; Hu, Xiaoping

doi:10.1016/s0730-725x(01)00428-3

Cited by 18 publications

(14 citation statements)

References 6 publications

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“…For fMRI, complex data are more widely available hence the detection, localization and correction procedures could be performed in the true k-space domain. A spike noise detection and correction method for fMRI data has already been described (12). ODD is expected to outperform such a spike detection and correction method by making more extensive use of the available information.…”

Section: Discussionmentioning

confidence: 99%

Robust correction of spike noise: Application to diffusion tensor imaging

Chavez

Storey

Graham

2009

Magnetic Resonance in Med

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Echo-planar imaging (EPI) -based diffusion tensor imaging (DTI)is particularly prone to spike noise. However, existing spike noise correction methods are impractical for corrupted DTI data because the methods correct the complex MRI signal, which is not usually stored on clinical MRI systems. The present work describes a novel Outlier Detection De-spiking technique (ODD) that consists of three steps: detection, localization, and correction. Using automated outlier detection schemes, ODD exploits the data redundancy available in DTI data sets that are acquired with a minimum of six different diffusion-weighted images (DWIs) with similar signal and noise properties. A mathematical formulation, describing the effects of spike noise on magnitude images, yields appropriate measures for an outlier detection scheme used for spike detection while a normalization-dependent outlier detection scheme is used for spike localization. ODD performs accurately on diverse DTI data sets corrupted by spike noise and can be used for automated control of DTI data quality. ODD can also be extended When a large transient, localized, erroneous change in k-space signal intensity occurs during MRI data collection, it is referred to as spike noise. Despite improvements to hardware, spike noise remains a relatively common, sporadic source of image artifact on many MRI systems. Due to the Fourier transform relation between MRI k-space and image space, spike noise creates a ripple artifact in corrupted MR images (1-3). The precise cause of spike noise is difficult to troubleshoot, because the noise results from numerous system/site imperfections that interact with the application of large, fast switching magnetic field gradients during imaging. Spike noise can appear as clustered or isolated events and can vary in severity from negligible to highly detrimental. In many cases, spike noise significantly obscures the information of interest in MR images, such that the images are discarded. It is particularly problematic for pulse sequences that require high gradient amplitude and slew rate, such as echo-planar imaging (EPI) -based diffusion tensor imaging (DTI) (4).At a given slice, DTI requires a set of at least six diffusion-weighted images (DWIs), sensitized to diffusion along different orientations, as well as a single T 2 -weighted image which is not diffusion sensitized (5,6). The voxel-wise signal magnitude of the DWIs is used to solve for six independent components, D xx , D xy , D xz , D yy , D yz , D zz of a symmetric 3 ϫ 3 tensor, characterizing three-dimensional (3D) diffusion. Typically, the tensor information is condensed into a spatially invariant scalar map such that a single image per slice represents the tensor data (6,7). In the brain, a commonly adopted scalar is the fractional anisotropy (FA: values from 0 to 1), which provides good contrast for white matter (high FA) and gray matter (low FA).Other diffusion tensor-derived scalars (7), as well as tractography (8), are receiving increasing attention. Analysis of the effects o...

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Section: Discussionmentioning

confidence: 99%

Robust correction of spike noise: Application to diffusion tensor imaging

Chavez

Storey

Graham

2009

Magnetic Resonance in Med

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“…Any remaining phase‐modulated FID signal was further suppressed by applying digital filter (cutoff frequency: 0.8/(2 T R ), filter order: 5) to the complex k ‐space data (5). Sparse spikes caused by gradient switching were removed from k ‐space data according to (6). EPI images were then reconstructed using the algorithm in (7).…”

Section: Methodsmentioning

confidence: 99%

Enhanced relative BOLD signal changes in T₂‐weighted stimulated echoes

Goerke

Moortele

Uğurbil

2007

Magnetic Resonance in Med

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The origin of the stimulus/task-induced signal changes in spin echo (SE) functional MRI (fMRI) at high magnetic fields is dynamic averaging due to diffusion in the presence of field gradients surrounding deoxyhemoglobin-containing microvasculature. The same mechanism is expected to be operative in stimulated echoes (STE). Compared to SE-fMRI, however, STEfMRI has the potential for larger diffusion weighting and consequently larger stimulus/task-induced signal changes as a result of an additional delay, the mixing time, T M . In the present study, functional signal changes were quantified for both primary echo (PRE) and STE as a function of echo and mixing time. The relative blood oxygenation level dependent (BOLD) signal changes in STE were larger than in PRE at the same echo time and increased with both mixing and echo time. The contrastto-noise ratio (CNR) of the STE, however, is close to the CNR of the PRE, indicating an increase of physiological noise with longer mixing times. In addition, the signal attenuation due to diffusion in the presence of magnetic field gradients near blood vessels was modeled using Monte Carlo simulations. Key words: stimulated echo; fMRI; transverse relaxation; T 2 ; dynamic averaging Gradient-echo (GE) echo-planar imaging (EPI) is commonly applied to probe neuronal activity with relatively high spatial and temporal resolution because this fast imaging method has a high signal-to-noise ratio (SNR) and exhibits large blood oxygenation level dependent (BOLD) signal changes. However, GE techniques are limited in their accuracy or specificity relative to the site of neuronal activity. In such functional magnetic resonance imaging (fMRI) experiments, the main mechanism causing BOLD signal changes is static dephasing, i.e., signal variation originating from phase differences between spins experiencing different magnetic fields near deoxyhemoglobincontaining blood vessels. As larger draining veins also contribute to the relative signal changes, the BOLD response can spread noticeably beyond the area of neuronal activity. Intravascular T 2 -changes, which are caused by spins diffusing in local field variations around and within deoxyhemoglobin-loaded red blood cells, add to the spatial blurring of the BOLD response.The BOLD signal variations are much more confined to the site of neuronal activity using moderately diffusionweighted spin echo (SE) imaging techniques. In these sequences, static dephasing is refocused and does not contribute to the signal change. The apparent T 2 is modulated through diffusion-induced dynamic averaging of the susceptibility gradients in the vicinity of capillaries and venules. Simulations and experiments demonstrated that T 2 -BOLD signal changes predominantly occur in the capillary bed (1,2). SE EPI is, therefore, assumed to be more specific to the site of neuronal activity. Specificity is further improved by mild diffusion-weighting to suppress intravascular signal contributions.SE-based imaging techniques, however, are hampered by the fact that the stimulu...

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“…The original time series from each voxel was high-pass filtered to compensate for slow drifts typical of fMRI signals and divided by its mean image value to compensate for variations in intensity with distance from the coil and to convert the data from arbitrary image intensity units to percent signal change. We discarded from further analysis trials whose fMRI responses were likely to be contaminated with eye blinks, abrupt head motion, or "k-space" spike noises, which can be introduced by potential imperfections in gradient coil, radiofrequency hardware, or other hardware components of an MRI scanner (Zhang et al 2001), by detecting high-frequency fluctuations in time series occurring simultaneously in the majority of voxels.…”

Section: Fmri Data Analysismentioning

confidence: 99%

Hierarchy of direction-tuned motion adaptation in human visual cortex

Lee

2012

Journal of Neurophysiology

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Lee HA, Lee SH. Hierarchy of direction-tuned motion adaptation in human visual cortex. J Neurophysiol 107: 2163-2184, 2012. First published January 4, 2012 doi:10.1152/jn.00923.2010.-Prolonged exposure to a single direction of motion alters perception of subsequent static or dynamic stimuli and induces substantial changes in behaviors of motion-sensitive neurons, but the origin of neural adaptation and neural correlates of perceptual consequences of motion adaptation in human brain remain unclear. Using functional magnetic resonance imaging, we measured motion adaptation tuning curves in a fine scale by probing changes in cortical activity after adaptation for a range of directions relative to the adapted direction. We found a clear dichotomy in tuning curve shape: cortical responses in early-tier visual areas reduced at around both the adapted and opposite direction, resulting in a bidirectional tuning curve, whereas response reduction in high-tier areas occurred only at around the adapted direction, resulting in a unidirectional tuning curve. We also found that the psychophysically measured adaptation tuning curves were unidirectional and best matched the cortical adaptation tuning curves in the middle temporal area (MT) and the medial superior temporal area (MST). Our findings are compatible with, but not limited to, an interpretation in which direct impacts of motion adaptation occur in both unidirectional and bidirectional units in early visual areas, but the perceptual consequences of motion adaptation are manifested in the population activity in MT and MST, which may inherit those direct impacts of adaptation from the directionally selective units. visual motion; visual cortex; direction selectivity; functional magnetic resonance imaging; V1; middle temporal area PROLONGED EXPOSURE TO A VISUAL pattern drifting in one direction alters perception of subsequently presented stimuli. Adaptation reduces the visibility of objects moving in the adapted direction (Raymond 1993;Tolhurst 1973), makes static or dynamic patterns appear to drift in the opposite direction (Mather et al. 1998), distorts the apparent speed of a moving stimulus (Stocker and Simoncelli 2009;Thompson 1981), or even shifts the perceived direction of motion away from the adapted direction (Levinson and Sekuler 1976).These profound alterations in perception suggest that adaptation has substantial impacts on the neural systems for processing motion stimuli. Indeed, there have been many attempts at uncovering neural consequences of motion adaptation. Although these studies have demonstrated that adaptation substantially modifies neural activities in retina (Barlow and Hill 1963) or visual areas in the cortex (Hammond et al. 1985;Huk et al. 2001;Petersen et al. 1985;Tolias et al. 2001), it has been quite challenging to single out a neural locus or mechanism for motion adaptation. Given the hierarchical and distributed nature of cortical motion processing, which has been supported by a body of anatomical (Felleman and van Essen 1991; Maunsell a...

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Elimination of k-space spikes in fMRI data

Cited by 18 publications

References 6 publications

Robust correction of spike noise: Application to diffusion tensor imaging

Robust correction of spike noise: Application to diffusion tensor imaging

Enhanced relative BOLD signal changes in T₂‐weighted stimulated echoes

Hierarchy of direction-tuned motion adaptation in human visual cortex

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Elimination of k-space spikes in fMRI data

Cited by 18 publications

References 6 publications

Robust correction of spike noise: Application to diffusion tensor imaging

Robust correction of spike noise: Application to diffusion tensor imaging

Enhanced relative BOLD signal changes in T2‐weighted stimulated echoes

Hierarchy of direction-tuned motion adaptation in human visual cortex

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Enhanced relative BOLD signal changes in T₂‐weighted stimulated echoes