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Wiley, Magnetic Resonance in Medicine, 6(62), p. 1597-1608, 2009

DOI: 10.1002/mrm.22106

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Complex‐valued analysis of arterial spin labeling–based functional magnetic resonance imaging signals

Journal article published in 2009 by Luis Hernandez‐Garcia ORCID, Alberto L. Vazquez, Daniel B. Rowe
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

Cerebral blood flow-dependent phase differences between tagged and control arterial spin labeling images are reported. A biophysical model is presented to explain the vascular origin of this difference. Arterial spin labeling data indicated that the phase difference is largest when the arterial component of the signals is preserved but is greatly reduced as the arterial contribution is suppressed by postinversion delays or flow-crushing gradients. Arterial vasculature imaging by saturation data of activation and hypercapnia conditions showed increases in phase accompanying blood flow increases.An arterial spin labeling functional magnetic resonance imaging study yielded significant activation by magnitude-only, phase-only, and complex analyses when preserving the whole arterial spin labeling signal. After suppression of the arterial signal by postinversion delays, magnitude-only and complex models yielded similar activation levels, but the phase-only model detected nearly no activation. When flow crushers were used for arterial suppression, magnitude-only activation was slightly lower and fluctuations in phase were dramatically higher than when postinversion delays were used.Although the complex analysis performed did not improve detection, a simulation study indicated that the complex-valued activation model exhibits combined magnitude and phase detection power and thus maximizes sensitivity under ideal conditions. This suggests that, as arterial spin labeling imaging and image correction methods develop, the complex-valued detection model may become helpful in signal detection.