Patient-Specific CFD Modeling of Cerebrospinal Fluid Dynamics in Lumbar Spinal Stenosis using Weight-Bearing MRI:...

Lumbar spinal stenosis (LSS) is a degenerative condition associated with significant back-pain and disability, which diagnosis relies mainly on radiological imaging (MRI, CT) and clinical examinations. However, grading systems, such as spinal canal obliteration, often show poor correlation with clinical symptoms. In recent studies, cauda equina nerve root dispersion caused by cerebrospinal fluid (CSF) dynamics at stenosis level demonstrated high agreement with clinical symptoms. However, no study has already evaluated the fluid dynamics of CSF and its relation with LSS. To fill this gap, this study aimed to analyze CSF dynamics through patient-specific computational fluid dynamics (CFD) models derived from supine and upright MRI imaging. Ten patients with LSS were recruited and divided into two cohorts based on stenosis severity (severe vs. moderate) and underwent supine and upright MRI (weight-bearing MRI). Spinal canal was segmented using a convolutional neural network (CNN) algorithm to enable the development of twenty patient-specific 3D mesh models. A mesh sensitivity analysis was performed to optimize computational accuracy and efficiency. With an optimal mesh size of 0.6mm, results showed significant differences in pressure gradients between severe and moderate cases (p = 0.001), as well as between vertical and horizontal positions (p = 0.005). Velocity analyses showed significantly higher velocities in severe cases (p = 0.0036) in respect to moderate ones. A strong negative correlation between maximum pressure and stenosis severity (r = -0.7698,p<0.001) highlighted the impact of anatomical narrowing on CSF fluid dynamics. This study demonstrates that CFD models could accurately analyze CSF dynamics in LSS, revealing significant fluid dynamics differences associated with stenosis severity and patient positioning. These findings under-score the importance of integrating patient-specific anatomical and physiological data into LSS management.Clinical relevanceThe use of CFD modeling in LSS pro-vides novel insights into the biomechanical and fluid dynamic implications of stenosis, offering potential advancements in diagnostic precision and personalized treatment planning.