Physiology recording with magnetic field probes for fMRI denoising

Physiological noise originating in cardiovascular and respiratory processes is a substantial confound in BOLD fMRI. When unaccounted for it reduces the temporal SNR and causes error in inferred brain activity and connectivity. Physiology correction typically relies on auxiliary measurements with per...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2017-07, Vol.154, p.106-114
Hauptverfasser: Gross, Simon, Vionnet, Laetitia, Kasper, Lars, Dietrich, Benjamin E., Pruessmann, Klaas P.
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Sprache:eng
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Zusammenfassung:Physiological noise originating in cardiovascular and respiratory processes is a substantial confound in BOLD fMRI. When unaccounted for it reduces the temporal SNR and causes error in inferred brain activity and connectivity. Physiology correction typically relies on auxiliary measurements with peripheral devices such as ECG, pulse oximeters, and breathing belts. These require direct skin contact or at least a tight fit, impairing subject comfort and adding to the setup time. In this work, we explore a touch-free alternative for physiology recording, using magnetic detection with NMR field probes. Placed close to the chest such probes offer high sensitivity to cardiovascular and respiratory dynamics without mechanical contact. This is demonstrated by physiology regression in a typical fMRI scenario at 7T, including validation against standard devices. The study confirms essentially equivalent performance of noise models based on conventional recordings and on field probes. It is shown that the field probes may be positioned in the subject's back such that they could be readily integrated in the patient table. [Display omitted] •An alternative method for physiology recording in fMRI is proposed.•Physiology signals are acquired by magnetic field sensing using NMR field probes.•For noise modeling, magnetic sensing is proven equivalent to standard recordings.•Being touch-free, magnetic sensing improves comfort and reduces setup times.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2017.01.022