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Abstract #4130

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

Javier Bisbal1,2,3, Julio Sotelo4, Hernan Mella5, Joaquin Mura6, Pablo Irarrazaval1,2,3,7, Cristián Tejos1,2,3, and Sergio Uribe1,8
1Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, 4Departamento de Informática, Universidad Técnica Federico Santa Maria, Santiago, Chile, 5School of Electrical Engineering, Pontificia Universidad Católica de Valparaiso, Valparaíso, Chile, 6Department of Mechanical Engineering,, Universidad Técnica Federico Santa Maria, Santiago, Chile, 7Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 8Department of Medical Imaging and Radiation Sciences, Monash University, Melbourne, Australia

Synopsis

Keywords: Analysis/Processing, Velocity & Flow, velocity enhancement, unwrapping, 4D Flow MRI

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

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Keywords