Characterization of Pulsatile Cerebrospinal Fluid Motion Among Young, Elderly and Idiopathic Normal Pressure Hydrocephalus By Correlation Mapping Technique
Satoshi Yatsushiro1, Saeko Sunohara2, Naokazu Hayashi3, Akihiro Hirayama3, Mitsunori Matsumae3, Afnizanfaizal Bin Abdullah4, and Kagayaki Kuroda2

1Course of Science and Technology, School of Science and Technology, Tokai University, Hiratsuka, Kanagawa, Japan, 2Course of Electrical and Electronic Engineering, Graduate School of Engineering, Tokai University, Hiratsuka, Kanagawa, Japan, 3Department of Neurosurgery, Tokai University School of Medicine, Isehara, Kanagawa, Japan, 4Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

Synopsis

Correlation mapping technique composed of delay time and correlation coefficient mapping to characterize propagation properties of cerebrospinal fluid (CSF) motion was applied to young, elderly healthy and idiopathic normal pressure hydrocephalus (iNPH) patient groups for classification. Brightness of the color of maximum correlation map was adjusted according to the amplitude of the CSF velocity waveform for assisting clinicians to understand the propagation properties intuitively. The groups were classified by quantifying the standard deviation of the correlation distributing in the intracranial CSF space. The technique was expected to classify diseases related to CSF dynamics such as iNPH.

Introduction

Understanding of cerebrospinal fluid (CSF) dynamics is important in brain pathophysiology. Visualization of CSF motion by four-dimensional velocity mapping (4D-VM) based on phase contrast technique has been performed.1-4 However, interpretation of the cine images is difficult because of the complex flow pattern in the CSF cavity structure. Therefore, a novel technique called correlation mapping, composed of delay time and correlation coefficient mapping, was proposed.5,6 This technique visualizes propagation of CSF motion from a particular region to the other spaces in still images depicting propagation delay and velocity waveform preservation. Since CSF is incompressible, the presence of the propagation delay and waveform change may be attributed to the compliance in the CSF space and brain parenchyma. Brain compliance decreases with age and/or development of idiopathic normal pressure hydrocephalus (iNPH).7,8 To characterize propagation properties of the pulsatile CSF motion of the elderly and iNPH in terms of compliance of CSF space, the correlation mapping technique was applied.

Methods

The present study was approved by the institutional review board (IRB). All the volunteers were examined after obtaining appropriate informed consent. The correlation mapping technique was applied to 11 young healthy (25±4 y-o), 12 elderly healthy (72±8 y-o), and 10 iNPH patient (76±6 y-o) subjects. Imaging by 4D-VM was performed on a clinical 1.5T scanner with the following conditions: flow-encoding directions, IS, RL and AP; TR, 9.7-13.7 ms; TE, 6.7-7.6 ms; FA, 20º; FOV, 28×28 cm2; reconstruction matrix, 320×320; slice thickness, 0.98 mm; VENC, 5-60 cm/s; temporal points in a cardiac cycle, 32; slice direction, sagittal. Image reconstruction was synchronized retrospectively based on the trigger pulses from electrocardiograph or a finger plethysmograph. The region for taking reference velocity waveform was set at a CSF region near the basilar artery because it has been reported that the pressure around this space is significantly higher than other CSF spaces.9 Cyclic color-coding was used in delay time map because CSF motion is periodic. It was expected that pulsation at certain point might arise earlier than that at the reference; therefore, delay time was represented as from -50% to 50% of a cardiac cycle. The average and the standard deviation of the correlation coefficient distributing in the intracranial CSF space above C1 level segmented by spatial-based fuzzy clustering10 were quantified. To assist clinicians to understand the propagation properties of the CSF motion, each voxel of the correlation coefficient maps was weighted with its brightness of the color; the brightness was lowered when peak-to-peak amplitude of the velocity waveform at an arbitrary point was lower than that at the reference, while it was not changed when the amplitude was higher or equal to.

Results

Delay time maps of the subjects exhibited pulsatile CSF motion propagation in the space with different compliance (Fig. 1a-c). The delay map in Fig. 1c showed an apparently more complicated distribution near the brainstem than the healthy volunteers. Maximum correlation maps presented difference of the propagation properties reflecting the CSF space compliance (Fig. 1d-f). The brightness was lower in the further spatial region from the reference region. The variation of the correlation coefficients in the patient group was clearly larger than those in the healthy groups. The average correlation values in the intracranial CSF space were plotted in Fig. 2. A significant difference between the young and the others appeared. The standard deviation represented in Fig. 3 exhibited significant differences among the three groups.

Discussion

The correlation mapping technique applied to the subjects demonstrated that CSF space compliance varied by aging and iNPH. In particular, delay time map of iNPH patient subject presented that iNPH varies and complicates the propagation properties.

Maximum correlation coefficient maps improved the amplitude-based clearly presented the propagation properties in the groups. Since the difference among the groups was revealed by the approach, which will help clinicians to understand the propagation properties and diagnose neurological diseases.

Quantification result of the average correlation reflected the variation of the compliance with aging, although the elderly healthy group and the iNPH group could not be differentiated. Decrease in the tissue compliance may lead to direct motion propagation, which lowers the spatial dispersion in the correlation distribution. Hence, standard deviation of the correlation distribution was quantified, and in consequence, the groups were classified.

In conclusions, the correlation mapping technique may classify iNPH group from the healthy group in view of the CSF space compliance. The technique will help clinicians to understand CSF dynamics and diagnose neurological diseases such as iNPH.

Acknowledgements

The authors thank Nao Kajihara and Misaki Hunato for their assistance with MR imaging.

References

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3. Matsumae M, Hirayama A, Atsumi H, Yatsushiro S, Kuroda K. Velocity and pressure gradients of cerebrospinal fluid assessed with magnetic resonance imaging. J Neurosurg 2014;120(1):218-227.

4. Hirayama A, Matsumae M, Yatsushiro S, Abdulla A, Atsumi H, Kuroda K. Visualization of Pulsatile CSF Motion Around Membrane-like Structures with both 4D Velocity Mapping and Time-SLIP Technique. Magn Reson Med Sci 2015; in press. doi: 10.2463/mrms.2014-0089.

5. Yatsushiro S, Hirayama A, Matsumae M, Kajiwara N, Abdullah A, Kuroda K. Correlation mapping for visualizing propagation of pulsatile CSF motion in intracranial space based on magnetic resonance phase contrast velocity images: preliminary results. In: Proceedings of the 36th Annual Meeting of IEEE EMBC, Cicago, USA, 2014. p 3300-3303

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10. Abdullah A, Hirayama A, Yatsushiro S, Matsumae M, Kuroda K. Cerebrospinal fluid image segmentation using spatial fuzzy clustering method with improved evolutionary Expectation Maximization. In: Proceedings of the 35th Annual Meeting of IEEE EMBC, Osaka, Japan, 2013. p 3359-3362

Figures

Figure 1. Results of delay time (a-c) and correlation coefficient (d-f) mapping in young (a, d) healthy, elderly healthy(b, e) and iNPH patient (c, f) subjects. Black rectangle delineates the reference region. Brightness of the maximum correlation map was varied according to peak-to-peak amplitude of the waveform.

Figure 2. Boxplot of the average of correlation coefficient distributing in the intracranial CSF space above the C1 level. There was a significant difference between the young group and the other groups (P<0.01).

Figure 3. Boxplot of the standard deviation of quantified correlation coefficient in the intracranial CSF space, which is the CSF space above the C1 level. A significant difference was recognized among the three groups (P<0.05).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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