Original contributionsThe respiratory modulation of intracranial cerebrospinal fluid pulsation observed on dynamic echo planar images☆
Introduction
The pressure of cerebrospinal fluid (CSF) changes with cardiac [1], [2], [3] and respiratory [4] rhythms. These are proposed to relate to the dynamics of CSF, including production, distribution and absorption [3], [4], [5], [6]. An understanding of these dynamics can help in addressing the common clinical brain problem of hydrocephalus [4], [5], [6], [7], [8], [9], [10]. Magnetic resonance imaging (MRI) provides a noninvasive, in vivo imaging technique for studying these rhythms in humans. Cardiac modulation of CSF has been intensively investigated using various MRI techniques [5], [6], [7], [8], [9], [10], [11], [12], but CSF pressure changes due to respiration, although studied in animals and humans for over 100 years [13], have been the subject of only a few MRI studies. Skroth and Klose [10], [13] detected respiration rhythm at the cerebral aqueduct and spinal canal using a one-dimensional gradient-echo pulse sequence. By applying spectral analysis to dynamic echo planar images (EPI) at selected regions of interest (ROIs), respiratory modulation of CSF pulsation has been observed at ventricles and the sagittal sinus [14], at the aqueduct [15] and at the aqueduct and basilar artery [16]. However, selection of ROIs requires knowledge of physiology and anatomy, and manual selection is time consuming.
For dynamic EPI, a respiratory movement of the thorax changes a human body's geometry and also changes the static magnetic field (B0) in the imaging space. The result is a periodical mismatch between processing frequency of the nuclei and the carrier frequency of the scanner receiver. A global phase modulation is detected on the dynamic images and at the center of k-space data [17], [18]. We postulate that CSF pulsation is passively modulated by the changes in intra-thoracic pressure secondary to respiration [4], [15], and that this pulsation can be observed as a flow-related enhancement on dynamic EPI.
Independent component (IC) analysis is a blind source separation technique [19]. It is described as a partial volume calculation technique when applied to analyze dynamic MR images [20], [21]. Tissues with different signal–time curves are automatically segmented, the output IC images are assumed to be spatially independent, and pixel values on an IC image are proportional to partial volumes for the corresponding tissue type. To investigate the spatiotemporal patterns of respiratory rhythms in human brains, we acquired dynamic EPI of twelve healthy volunteers, who performed normal-breathing and breath-holding experiments during MR scanning. The images were analyzed using spectral analysis, spectroscopic images, IC analysis and signal measurements in selected ROIs.
Section snippets
Materials and methods
Dynamic EPI of 12 normal volunteers (seven male and five female), who performed normal-breathing and breath-holding experiments, were acquired on a 1.5-T clinical scanner (Signa CV/i, GE Medical Systems, Milwaukee, WI, USA). Written informed consent was obtained from all subjects. A quadrature birdcage coil and a single-shot, gradient-echo EPI pulse sequence were used. Scan parameters were TE/TR=60/200 ms, flip angle=90°, field of view=24×24 cm, image matrix=128×128, slice thickness=5 mm and
Results
Amplitude spectra and spectroscopic images for a normal-breathing experiment performed by a 23-year-old male volunteer are illustrated in Fig. 1. The amplitude spectra of the three masks displayed in (E) provided information about respiratory and cardiac frequency bands. The two spectroscopic images demonstrate that respiratory rhythm occurred at the ventricle, CSF space and sagittal sinus (F), and that cardiac rhythm occurred at vessel, choroid plexus and ventricle (G).
Fig. 2 displays FastICA
Discussion
The averaged amplitude spectra of the three masks are sensitive in detecting frequency bands of physiological rhythms. Spectroscopic images can be generated for displaying spatial locations of these physiological signals. However, temporal signal changes are not available from either amplitude spectra or spectroscopic images. As a consequence, previous studies use measures of signal–time curves in selected ROIs to illustrate temporal signal changes [10], [13], [14], [15], [16]. But inverted
Conclusion
In this study, we validated our postulate that CSF pulsation is passively modulated by intra-thoracic rhythmic pressure changes secondary to respiration. We found that (1) cardiac rhythms appear at pixels in the vicinity of intracranial vessels, choroid plexus, ventricle, CSF spaces and sagittal sinus; (2) respiratory rhythms appear at pixels in the vicinity of ventricle, CSF spaces and sagittal sinus; (3) breath-holding induces vessel dilation; (4) respiration-modulated CSF pulsation at
Acknowledgments
We thank Mr. Kuo-Ching Liu for providing an image-sorting program to convert DICOM images into Matlab format. We also thank the laboratory of Computer and Information Science, Department of Computer Science and Engineering, at the Helsinki University of Technology, Finland, for providing the FastICA package as freeware.
References (28)
Vascular factor in intracranial pressure and maintenance of cerebrospinal fluid circulation
Brain
(1943)Pulsatile movements in the CSF pathways
Br J Radiol
(1966)- et al.
Human brain motion and cerebrospinal fluid circulation demonstrated with MR velocity imaging
Radiology
(1987) Cerebrospinal fluid dynamics in infants evaluated with color Doppler US and spectral analysis: respiratory versus arterial synchronization
Radiology
(1994)Cerebrospinal fluid circulation and associated intracranial dynamics. A radiologic investigation using MR imaging and radionuclide cisternography
Acta Radiol
(1993)- et al.
A new view on the CSF-circulation with the potential for pharmacological treatment of childhood hydrocephalus
Acta Pediatr
(1997) - et al.
Flow dynamics of cerebrospinal fluid: assessment with phase-contrast velocity MR imaging performed with retrospective cardiac gating
Radiology
(1992) Modern concepts of brain motion and cerebrospinal fluid flow
Radiology
(1992)- et al.
Brain parenchyma motion: measurement with cine echo-planar MR imaging
Radiology
(1992) - et al.
Cerebrospinal fluid flow: III. Pathological cerebrospinal fluid pulsations
Neuroradiology
(1992)
Brain motion: measurement with phase-contrast MR imaging
Radiology
Cerebrospinal fluid flow: I. Physiology of cardiac-related pulsation
Neuroradiology
Cerebrospinal fluid flow: II. Physiology of respiration-related pulsations
Neuroradiology
Power spectrum analysis of functionally-weighted MR data: what's in the noise?
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Grant sponsors: National Science Council, ROC; Grant number: 92-2314-B-010-023.