B0-fluctuation-induced temporal variation in EPI image series due to the disturbance of steady-state free precession

Zhao, Xiaoli; Bodurka, Jerzy; Jeśmanowicz, A; Li, Shi‐Jiang

doi:10.1002/1522-2594(200011)44:5<758::aid-mrm14>3.0.co;2-g

Cited by 46 publications

(37 citation statements)

References 27 publications

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“…Krueger and Glover demonstrate that flip-angle reduction directly moves the acquisition toward the thermal noise dominated state. (Kruger and Glover, 2001) Shorter TR times do however lead to greater potential for steady-state coherence pathways between measurements that could be disturbed by B 0 changes driven by the cardiac and respiration cycles (Zhao et al 2000), and therefore could also re-introduce spatial correlations in acquisitions that are not properly spoiled through this mechanism.…”

Section: Strategies To Reduce the Physiological To Thermal Noise Ratiomentioning

confidence: 99%

Impacting the effect of fMRI noise through hardware and acquisition choices – Implications for controlling false positive rates

Wald

Polimeni

2017

NeuroImage

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We review the components of time-series noise in fMRI experiments and the effect of image acquisition parameters on the noise. In addition to helping determine the total amount of signal and noise (and thus temporal SNR), the acquisition parameters have been shown to be critical in determining the ratio of thermal to physiological induced noise components in the time series. Although limited attention has been given to this latter metric, we show that it determines the degree of spatial correlations seen in the time-series noise. The spatially correlations of the physiological noise component are well known, but recent studies have shown that it can lead to a higher than expected false-positive rate in cluster-wise inference based on parametric statistical methods used by many researchers. Based on understanding the effect of acquisition parameters on the noise mixture, we propose several acquisition strategies that might be helpful reducing this elevated false-positive rate, such as moving to high spatial resolution or using highly-accelerated acquisitions where thermal sources dominate. We suggest that the spatial noise correlations at the root of the inflated false-positive rate problem can be limited with these strategies, and the well-behaved spatial autocorrelation functions (ACFs) assumed by the conventional statistical methods are retained if the high resolution data is smoothed to conventional resolutions.

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Section: Strategies To Reduce the Physiological To Thermal Noise Ratiomentioning

confidence: 99%

Impacting the effect of fMRI noise through hardware and acquisition choices – Implications for controlling false positive rates

Wald

Polimeni

2017

NeuroImage

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“…It is worthwhile to note that, due to the fact that a relatively short TR of 1 s was used for data acquisition, another potential source of signal variance could arise from disturbances in the steady state free procession (SSFP) caused by the temporal variations in B 0 (Zhao et al, 2000). This would most likely be expected to occur in cerebrospinal fluid (CSF), which has a relatively long T 2 .…”

Section: Discussionmentioning

confidence: 99%

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series

Hahn

Rowe

2012

NeuroImage

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As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase.

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“…However, it is possible to modify the pulse sequence such that the InI projection alternates the partition encoding direction dynamically to generate mInI data to study non-stationary features of fMRI, such as trial-by-trial variability. It should be noted that mInI using either identical or different encoding axes can develop steady state free precession (SSFP) signal as long as there is a train of RF pulses with TR < T2 (Zhao et al, 2000). B 0 fluctuations originating from respiration, physical movement outside the FOV, and system instability can cause SSFP temporal variation.…”

Section: Discussionmentioning

confidence: 99%

“…B 0 fluctuations originating from respiration, physical movement outside the FOV, and system instability can cause SSFP temporal variation. It has been suggested that a strong crusher can be used to minimize these temporal noise fluctuations (Zhao et al, 2000). In fact, our experiment used a crusher gradient of 20 mT/m strength and 10 ms duration, as previously suggested (Zhao et al, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…It has been suggested that a strong crusher can be used to minimize these temporal noise fluctuations (Zhao et al, 2000). In fact, our experiment used a crusher gradient of 20 mT/m strength and 10 ms duration, as previously suggested (Zhao et al, 2000). However, since the suggested strong crusher was studied with TR = 200 ms, InI and mInI may require further increase the crusher moment to reduce the SSFP signal disturbance by increasing the crusher duration or strength.…”

Section: Discussionmentioning

confidence: 99%

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Multi-projection magnetic resonance inverse imaging of the human visuomotor system

et al. 2012

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Using highly parallel radiofrequency (RF) detection, magnetic resonance inverse imaging (InI) can achieve 100 ms temporal resolution with whole brain coverage. This is achieved by trading off partition encoding steps and thus spatial resolution for a higher acquisition rate. The reduced spatial information is estimated by solving under-determined inverse problems using RF coil sensitivity information. Here we propose multi projection inverse imaging (mInI) to combine different projection images to improve the spatial resolution of InI. Specifically, coronal, sagittal, and transverse projection images were acquired from different runs of the fMRI acquisitions using a 32-channel head coil array. Simulations show that mInI improves the quality of the instantaneous image reconstruction significantly. Going from one projection to three projections, the spatial resolution quantified by the full width at half maximum of the point-spread function (PSF) is improved from 2.6 pixels to 1.4 pixels (4 mm nominal resolution per pixel). Considering the shape of the PSF, the effective spatial resolution is improved from 16.9 pixels to 4.7 pixels. In vivo fMRI experiments using a two-choice reaction time tasks shows visual and sensorimotor cortical activity spatially consistent with typical EPI data, yet mInI offers 100 ms temporal resolution with the whole brain coverage. The mInI data with three projections revealed that the sensorimotor cortex was activated 700 ms after the visual cortex. mInI can be applied to BOLD-contrast fMRI experiments to characterize the dynamics of the activated brain areas with a high spatiotemporal resolution.

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B0-fluctuation-induced temporal variation in EPI image series due to the disturbance of steady-state free precession

Cited by 46 publications

References 27 publications

Impacting the effect of fMRI noise through hardware and acquisition choices – Implications for controlling false positive rates

Impacting the effect of fMRI noise through hardware and acquisition choices – Implications for controlling false positive rates

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series

Multi-projection magnetic resonance inverse imaging of the human visuomotor system

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