Automated Cerebrospinal Fluid Flow Quantification in Hydrocephalus Using Phase‐Contrast Magnetic Resonance Imaging

ABSTRACT Quantitative cerebrospinal fluid (CSF) flow analysis with phase‐contrast magnetic resonance imaging depends on segmentation and is affected by operator variability. This cohort method‐comparison study evaluated whether a flow‐aware deep learning (DL) model provides reproducible C2–C3 CSF segmentation and flow quantification compared with repeated manual segmentation. Ten hydrocephalus patients underwent 3‐T phase‐contrast magnetic resonance imaging at C2–C3, with 32 magnitude and phase frames across the cardiac cycle. Three experts and one novice segmented each case twice using semi‐automatic software, generating 80 manual annotations. A pretrained flow‐aware 2D‐UNet generated one fixed automatic segmentation per case. The inference pipeline was deterministic, meaning that repeated processing of the same input with fixed model weights and settings produced identical masks. A multi‐expert reference was defined as the arithmetic mean of the expert measurements. Region of interest (ROI) surface area, peak flow amplitude, and stroke volume were compared using mean difference, Bland–Altman 95% limits of agreement, Lin's concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC). Expert ICC(1,1) was 0.88–0.97 for surface area, 0.97–1.00 for peak flow amplitude, and 0.97–0.99 for stroke volume; novice values were 0.63, 0.95, and 0.92, respectively. Compared with the multi‐expert reference, model mean differences were −0.42 mm 2 for surface area, −0.00 mL/s for peak flow amplitude, and −0.01 mL for stroke volume, with CCC values of 0.50, 0.87, and 0.86. Bland–Altman limits of agreement were −69.12 to 68.27 mm 2 , −1.47 to 1.46 mL/s, and −0.34 to 0.31 mL. The model provided rapid, deterministic CSF segmentation with small cohort‐level mean differences in peak flow amplitude and stroke volume; however, individual‐level agreement with the flow‐derived multi‐expert reference was lower than for expert and novice segmentations obtained using the semi‐automatic workflow.