Modeling CSF Outflow: A Bi-Component Approach with Time-SLIP MRI

Motivation: Accurately quantifying cerebrospinal fluid (CSF) outflow is essential for understanding its role in brain health and neurodegenerative diseases. Existing models fail to capture the complex dynamics of CSF flow. Goal(s): Develop a robust model to better characterize CSF outflow, distinguishing fast bulk flow from slower, perfusion-like components. Approach: We propose a bi-component model combining Gaussian and perfusion in general kinetic model (GKM) from gamma variate functions to represent fast and slow CSF outflow. Phantom studies and in-vivo imaging of healthy subjects using Time-SLIP technique. Results: The bi-component model accurately fitted CSF outflow data, effectively distinguishing distinct flow components. Impact: The bi-component model provides a more accurate characterization of CSF outflow dynamics, distinguishing fast and slow flow components. It offers potential for future studies on age-related changes in CSF outflow, aiding in early biomarkers in neurodegenerative disease research.