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

Authors

Eder Alejandro Rodríguez Martínez, Pan Liu, Kimi Owashi, Antoine Gayet, Olivier Balédent

Published in

NMR in biomedicine. Volume 39. Issue 12.

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 mm² 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 mm², -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.

PMID:
42929072
Bibliographic data and abstract were imported from PubMed on 26 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.