Abstract
Purpose: Respiration-related CSF flow through the cerebral aqueduct may be useful for elucidating physiology and pathophysiology of the glymphatic system, which has been proposed as a mechanism of brain waste clearance. Therefore, we aimed to (1) develop a real-time (CSF) flow imaging method with high spatial and sufficient temporal resolution to capture respiratory effects, (2) validate the method in a phantom setup and numerical simulations, and (3) apply the method in vivo and quantify its repeatability and correlation with different respiratory conditions. Methods: A golden-angle radial flow sequence (reconstructed temporal resolution 168 ms, spatial resolution 0.6 mm) was implemented on a 7T MRI scanner and reconstructed using compressed sensing. A phantom setup mimicked simultaneous cardiac and respiratory flow oscillations. The effect of temporal resolution and vessel diameter was investigated numerically. Healthy volunteers (n = 10) were scanned at four different respiratory conditions, including repeat scans. Results: Phantom data show that the developed sequence accurately quantifies respiratory oscillations (ratio real-time/reference QR = 0.96 ± 0.02), but underestimates the rapid cardiac oscillations (ratio QC = 0.46 ± 0.14). Simulations suggest that QC can be improved by increasing temporal resolution. In vivo repeatability was moderate to very strong for cranial and caudal flow (intraclass correlation coefficient range: 0.55–0.99) and weak to strong for net flow (intraclass correlation coefficient range: 0.48–0.90). Net flow was influenced by respiratory condition (p < 0.01). Conclusions: The presented real-time flow MRI method can quantify respiratory-related variations of CSF flow in the cerebral aqueduct, but it underestimates rapid cardiac oscillations. In vivo, the method showed good repeatability and a relationship between flow and respiration.
Original language | English |
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Pages (from-to) | 770-786 |
Number of pages | 17 |
Journal | Magnetic Resonance in Medicine |
Volume | 88 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2022 |
Subject classification (UKÄ)
- Medical Image Processing
Free keywords
- cerebrospinal fluid
- CSF
- flow
- magnetic resonance imaging
- MRI
- real-time