Enhancing the utility of complex-valued functional magnetic resonance imaging detection of neurobiological processes through postacquisition estimation and correction of dynamic B0 errors and motion
Human Brain Mapping
Functional magnetic resonance imaging (fMRI) time series analysis is typically performed using only the magnitude portion of the data. The phase information remains unused largely due to its sensitivity to temporal variations in the magnetic field unrelated to the functional response of interest. These phase changes are commonly the result of physiologic processes such as breathing or motion either inside or outside the imaging field of view. As a result, although the functional phase response carries pertinent physiological information concerning the vasculature, one aspect of which is the location of large draining veins, the full hemodynamic phase response is understudied and is poorly understood, especially in comparison with the magnitude response. It is likely that the magnitude and phase contain disjoint information, which could be used in tandem to better characterize functional hemodynamics. In this work, simulated and human fMRI experimental data are used to demonstrate how statistical analysis of complex-valued fMRI time series can be problematic, and how robust analysis using these powerful and flexible complex-valued statistics is possible through postprocessing with correction for dynamic magnetic field fluctuations in conjunction with estimated motion parameters. These techniques require no special pulse sequence modifications and can be applied to any complex-valued echo planar imaging data set. This analysis shows that the phase component appears to contain information complementary to that in the magnitude and that processing and analysis techniques are available to investigate it in a robust and flexible manner.
Hahn, Andrew D.; Nencka, Andrew S.; and Rowe, Daniel B., "Enhancing the utility of complex-valued functional magnetic resonance imaging detection of neurobiological processes through postacquisition estimation and correction of dynamic B0 errors and motion" (2012). Mathematics, Statistics and Computer Science Faculty Research and Publications. 26.
Human Brain Mapping, Vol. 33, No. 2 (February 2012):288-306. DOI