ArticleFluids and barriers of the CNS2026
Bi-component modeling of cerebrospinal fluid outflow using Time-SLIP MRI.
Article in Fluids and barriers of the CNS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundUnderstanding egress outflow of cerebrospinal fluid (CSF) is essential for exploring its role in brain health and its relationship to neurodegenerative diseases. Existing models often fail to capture the complex flow dynamics, particularly distinguishing rapid bulk flow from slower, perfusion-like components. This study aims to develop and validate a bi-component model that characterizes these distinct signal patterns using Time-Spatial Labeling Inversion Pulse (Time-SLIP) MRI data.
methodsA bi-component model is proposed by combining a Gaussian function (to represent fast bulk outflow) with a Г-variate function (representing slower, perfusion-like flow). Validation phantom studies were conducted using a sealed water phantom with computer-controlled bulk flow. In-vivo meninges images in eight healthy subjects were acquired on a clinical 3T MRI using Time-SLIP with 3D single-shot FSE. Signal Increase Ratio (SIR) from these experiments were fitted using the proposed bi-component model with fitting accuracy assessed using coefficient of determination (R2), Sum of Squared estimate of Errors (SSE), and Root Mean Square Error (RMSE).
resultsPhantom studies showed superior fitting of bulk fluid dynamics by the Gaussian component, confirmed by lower SSE, RMSE and higher R2. The in-vivo results demonstrated that the bi-component model provided a superior fit, capturing two distinct components of CSF outflow, which are hypothesized to correspond to fast bulk flow and a slower, perfusion-like component, which could be a mixture of CSF and interstitial fluid.
conclusionThe bi-component model enhances characterization of CSF egress outflow from intrinsic labeling by Time-SLIP, providing potential quantitative biomarkers for the assessment of neurodegenerative diseases.
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