Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

Olivier Reynaud, João Jorge, Rolf Gruetter, José P Marques, W. Van der Zwaag

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgaveArtikelWetenschappelijkpeer review

Samenvatting

PURPOSE: Physiological noise often dominates the blood-oxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways.

METHODS: The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-to-noise ratio (tSNR) and network characterization based on seed correlation and independent component analysis.

RESULTS: Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6- to 8-fold).

CONCLUSION: After physiological noise correction, 2D- and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Originele taal-2Engels
Pagina's (van-tot)888-896
TijdschriftMagnetic Resonance in Medicine
Volume78
Nummer van het tijdschrift3
DOI's
StatusGepubliceerd - 2017

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