Cardiac-driven Pulsatile Motion of Intracranial Cerebrospinal Fluid Visualized Based on a Correlation Mapping Technique.

PURPOSE: A correlation mapping technique delineating delay time and maximum correlation for characterizing pulsatile cerebrospinal fluid (CSF) propagation was proposed. After proofing its technical concept, this technique was applied to healthy volunteers and idiopathic normal pressure hydrocephalus (iNPH) patients.

METHODS: A time-resolved three dimensional-phase contrast (3D-PC) sampled the cardiac-driven CSF velocity at 32 temporal points per cardiac period at each spatial location using retrospective cardiac gating. The proposed technique visualized distributions of propagation delay and correlation coefficient of the PC-based CSF velocity waveform with reference to a waveform at a particular point in the CSF space. The delay time was obtained as the amount of time-shift, giving the maximum correlation for the velocity waveform at an arbitrary location with that at the reference location. The validity and accuracy of the technique were confirmed in a flow phantom equipped with a cardiovascular pump. The technique was then applied to evaluate the intracranial CSF motions in young, healthy (N = 13), and elderly, healthy (N = 13) volunteers and iNPH patients (N = 13).

RESULTS: The phantom study demonstrated that root mean square error of the delay time was 2.27%, which was less than the temporal resolution of PC measurement used in this study (3.13% of a cardiac cycle). The human studies showed a significant difference (P < 0.01) in the mean correlation coefficient between the young, healthy group and the other two groups. A significant difference (P < 0.05) was also recognized in standard deviation of the correlation coefficients in intracranial CSF space among all groups. The result suggests that the CSF space compliance of iNPH patients was lower than that of healthy volunteers.

CONCLUSION: The correlation mapping technique allowed us to visualize pulsatile CSF velocity wave propagations as still images. The technique may help to classify diseases related to CSF dynamics, such as iNPH.