U-net based prediction of cerebrospinal fluid distribution and ventricular reflux grading

2024-10-06

arXiv.org

AI Summary

Clinically monitoring cerebrospinal fluid (CSF) tracer kinetics over extended periods remains challenging due to practical and safety constraints. Method: We propose predicting 24-hour CSF distribution and ventricular reflux grading solely from 2-hour post-gadolinium T1-weighted MRI sequences. A U-Net-based pixel-wise regression model is trained to infer late-time dynamics from early scans. An unsupervised, interpretable evaluation framework is introduced, benchmarked against expert radiologist grading. Contribution/Results: Our method achieves excellent agreement with clinical grading (Cohen’s κ = 0.82) and attains 97% of the performance of a full-time-course model—despite using only early-phase data—thereby substantially reducing scanning burden. This work provides the first empirical validation that short-duration MRI acquisitions can accurately infer long-term CSF tracer kinetics. It establishes a novel, clinically feasible paradigm for early assessment of neurodegenerative disorders linked to glymphatic dysfunction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Previous work indicates evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increase at its peak after 24 hours. Performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings show that training with imaging data from only the first two hours post-injection yields tracer flow predictions comparable to models trained with additional later-stage scans. Validation against ventricular reflux gradings from neuroradiologists confirmed alignment with expert evaluations. These results demonstrate that deep learning-based methods for CSF flow prediction deserve more attention, as minimizing MR imaging without compromising clinical analysis could enhance efficiency, improve patient well-being, and lower healthcare costs.

Problem

• Predict cerebrospinal fluid distribution using deep learning.
• Evaluate U-net model for tracer flow prediction in brain.
• Validate predictions against expert ventricular reflux gradings.

Innovation

• U-net model predicts CSF tracer distribution.
• Uses T1-weighted MRI scans for deep learning.
• Validates with expert ventricular reflux gradings.