Origin of subarachnoid cerebrospinal fluid pulsations: a phase-contrast MR analysis

Henry–Feugeas, Marie-Cécile; Idy-Peretti, I.; Balédent, O.; Poncelet–Didon, Anne; Zannoli, G.; Bittoun, J.; Schouman-Claeys, É.

doi:10.1016/s0730-725x(99)00142-3

Cited by 105 publications

(76 citation statements)

References 23 publications

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“…5,6,16,32 Moreover, in the normal intracranial environment, synchronization among arterial, CSF, and venous velocity waveforms has been demonstrated by evaluation of the velocities of these components at the axial slice of the cervical region. [13][14][15]17 However, the Windkessel effect should prevent transmission of arterial pulse waveforms to CSF through the capillaries 32 because the compliance of the arterial wall will change pulsatile arterial flow to stationary flow.…”

Section: Discussionmentioning

confidence: 99%

“…In general, pulsatile CSF motion is considered a consequence of the pulsatile expansion of brain tissue driven by the filling of the vascular bed by arterial blood or the pulsation of the major basal cerebral arteries, 1,3 the choroid plexus within the ventricles, 4 or the epidural venous plexus in the spine. 5 However, the principal driving forces of CSF pulsation and the transmission mechanism of the force from the blood vessels or brain parenchyma are not fully understood. Evaluation of normal CSF circulation in terms of peak velocity, flow rate, and flow velocity waveform in conjunction with evaluation of spatial blood flow may provide a useful knowledge base for diagnosing patients with disorders of CSF dynamics, such as hydrocephalus, 6,7 Chiari I malformation, 8,9 and arachnoid cysts.…”

Section: Introductionmentioning

confidence: 99%

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Visualization of Pulsatile CSF Motion Around Membrane-like Structures with both 4D Velocity Mapping and Time-SLIP Technique

et al. 2015

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Purpose: We compared the depiction of pulsatile CSF motion obtained by 4-dimensional phase-contrast velocity mapping (4D-VM) with that by time-spatial labeling inversion pulse (time-SLIP) technique in the presence of membrane structures.Materials and Methods: We compared the 2 techniques using a flow phantom comprising tubes with and without a thin rubber membrane and applied the techniques to 6 healthy volunteers and 2 patients to analyze CSF dynamics surrounding thin membrane structures, such as the Liliequist membrane (LM), or the wall of an arachnoid cyst.Results: Phantom images exhibited propagation of the flow and pressure gradient beyond the membrane in the tube. In contrast, fluid labeled by the time-SLIP technique showed little displacement from the blockage of spin travelling by the membrane. A similar phenomenon was observed around the LM in healthy volunteers and the arachnoid cyst wall in a patient.Conclusion: Four-dimensional phase-contrast velocity mapping permitted visualization of the propagation of CSF pulsation through the intracranial membranous structures. This suggests that 4D-VM and the time-SLIP technique provide different information on flow and that both techniques are useful for classifying the pathophysiological status of CSF and elucidating the propagation pathway of CSF pulsation in the cranium.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visualization of Pulsatile CSF Motion Around Membrane-like Structures with both 4D Velocity Mapping and Time-SLIP Technique

et al. 2015

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“…In contrast, in the narrow posterior cervical SAS, the predominantly venous in‰uence on CSF motion and posterior arachnoid septations may prevent deˆnable oscillatory motion of the CSF. 24 Consequently, the CSF lumen area would be underestimated. On the other hand, the CSF lumen area in the cerebral aqueduct obtained in this study was 6.15±2.52 mm 2 (Table 4), similar to that reported by Baledent and colleagues (8±2 mm 2 ).…”

Section: Discussionmentioning

confidence: 99%

Phase-contrast MR Studies of CSF Flow Rate in the Cerebral Aqueduct and Cervical Subarachnoid Space with Correlation-based Segmentation

Yoshida

Takahashi

Saijo

et al. 2009

MRMS

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Purpose: Accurate measurement of cerebrospinal ‰uid (CSF) ‰ow rate elucidates pathophysiological changes in the intracranial environment and is thus clinically useful. We investigated the feasibility of correlation coe‹cient (CC) analysis for extracting CSF lumens in the cerebral aqueduct and cervical subarachnoid space (SAS) to quantify CSF ‰ow rate and net ‰ow from data acquired by phase-contrast magnetic resonance imaging (PC-MRI).Methods: First, in phantom studies on pulsatile ‰ow using a 1.5-tesla MR imaging system, we investigated the accuracy of CC analysis and used a statistical approach to determine an optimal threshold value for extracting the CSF lumens (CCmin). Second, we performed phantom studies on constant ‰ow with various ‰ow rates to estimate the accuracy of low ‰ow measurement by PC-MRI. Finally, in 6 healthy male volunteers aged 24±2 years, we estimated the CSF lumen areas, net ‰ows, and peak ‰ow rates in the cerebral aqueduct and cervical SAS using CC analysis with the optimal CCmin value determined in phantom studies. Three observers analyzed results to compare reproducibility of CC analysis with that of manual segmentation.Results: The optimal CCmin value for CC analysis was 0.41 for a matrix measuring 256×256. The CSF lumen area extracted by CC analysis was 6.15±2.52 mm 2 , and the net ‰ow in the cerebral aqueduct was 0.74±0.38 mL/min; in the cervical SAS, lumen area was 135.60±17.94 mm 2 and net ‰ow, 12.55±12.67 mL/min. The reproducibility of CSF lumen extraction was better by CC analysis than manual segmentation.Conclusion: CC analysis oŠers a quick and reproducible method for segmenting CSF lumens and calculating CSF ‰ow rate.

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“…This sequence of processing steps optimised the combined effects of these procedures and worked well at lower field strength too (Barry, personal communication), so it will be interesting to formally test whether this might indeed be the optimal sequence of denoising steps at every field strength (Jones et al, 2008), since the influence of physiological noise varies with field strength (Triantafyllou et al, 2005). It is also important to note that extracting several components from the CSF surrounding the spinal cord is potentially a sensible approach, because CSF flow is not homogeneous but is instead organized into distinct channels with different time-profiles (Henry-Feugeas et al, 2000;Schroth and Klose, 1992).…”

Section: Data-driven Approaches To Physiological Noise Correctionmentioning

confidence: 99%

Denoising spinal cord fMRI data: Approaches to acquisition and analysis

et al. 2017

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractFunctional magnetic resonance imaging (fMRI) of the human spinal cord is a difficult endeavour due to the cord's small cross-sectional diameter, signal drop-out as well as image distortion due to magnetic field inhomogeneity, and the confounding influence of physiological noise from cardiac and respiratory sources. Nevertheless, there is great interest in spinal fMRI due to the spinal cord's role as the principal sensorimotor interface between the brain and the body and its involvement in a variety of sensory and motor pathologies. In this review, we give an overview of the various methods that have been used to address the technical challenges in spinal fMRI, with a focus on reducing the impact of physiological noise. We start out by describing acquisition methods that have been tailored to the special needs of spinal cord fMRI and aim to increase the signal-to-noise ratio and reduce distortion in obtained images. Following this, we concentrate on image processing and analysis approaches that address the detrimental effects of noise. While these include variations of standard pre-processing methods such as motion correction and spatial filtering, the main focus lies on denoising techniques that can be applied to taskbased as well as resting-state data sets. We review both model-based approaches that rely on externally acquired respiratory and cardiac signals as well as data-driven approaches that estimate and correct for noise using the data themselves. We conclude with an outlook on techniques that have been successfully applied for noise reduction in brain imaging and whose use might be beneficial for fMRI of the human spinal cord.3

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Origin of subarachnoid cerebrospinal fluid pulsations: a phase-contrast MR analysis

Cited by 105 publications

References 23 publications

Visualization of Pulsatile CSF Motion Around Membrane-like Structures with both 4D Velocity Mapping and Time-SLIP Technique

Visualization of Pulsatile CSF Motion Around Membrane-like Structures with both 4D Velocity Mapping and Time-SLIP Technique

Phase-contrast MR Studies of CSF Flow Rate in the Cerebral Aqueduct and Cervical Subarachnoid Space with Correlation-based Segmentation

Denoising spinal cord fMRI data: Approaches to acquisition and analysis

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