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

TL;DR

This study employs a U-net deep learning model to predict cerebrospinal fluid distribution in the brain using MRI scans, potentially reducing imaging time while maintaining clinical accuracy.

Contribution

It introduces a novel U-net based approach for pixel-wise prediction of CSF flow from early MRI data, demonstrating effective predictions with minimal imaging stages.

Findings

Training with only early post-injection data yields comparable results to full data models.

Predictions align well with expert ventricular reflux assessments.

Method could reduce MRI imaging time without losing diagnostic accuracy.

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.