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This is a Experimental Study study.

September 17, 2025

4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks

Key Points

The model significantly reduces velocity wrapping artifacts, leading to more accurate assessments.
Performance was superior to traditional methods, enhancing the reliability of 4D flow MRI.
The approach utilizes a divergence-free neural network for improved signal-to-noise ratio.
In vivo results demonstrate potential for better clinical applications in hemodynamics.

Abstract

Motivation: 4D flow MRI suffers from different sources of noise and wrapping artifacts which can affect its accuracy and usability as a clinical tool. Goal(s): This study aims to simultaneously improve signal-to-noise ratio and fix velocity wrapping artifacts in 4D Flow MRI. Approach: We developed an unsupervised neural network that enhances 4D Flow MRI by estimating a divergence-free velocity field. Results: The model demonstrated superior performance compared to existing methods, and initial in vivo results validated its potential for more reliable, artifact-free hemodynamic assessments in clinical applications. Impact: We proposed an unsupervised divergence-free neural network that effectively enhances the signal-to-noise ratio and reduces velocity wrapping artifacts in 4D Flow MRI, improving its accuracy and reliability in both clinical and research settings

4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks

Key Points

Abstract

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