Abstract #4612

PINN with Divergence-Free Vector Potential for Velocity Fields Denoising in 4D flow MRI

Javier Bisbal^1,2,3, Joaquin Mura⁴, Julio Sotelo^1,3,5, Hernán Mella^3,6, Cristóbal Arrieta^1,2,3, Pablo Irarrazaval^1,2,3,7, and Sergio Uribe^1,3,8

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, ⁴Department of Mechanical Engineering, Universidad Técnica Federico Santa Maria, Santiago, Chile, ⁵School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile, ⁶School of Electrical Engineering, Pontificia Universidad Católica de Valparaiso, Valparaíso, Chile, ⁷Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ⁸Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

Synopsis

Keywords: Data Processing, Machine Learning/Artificial Intelligence4D flow MRI suffers from different sources of noise and aliasing artifacts. Recent denoising techniques are time-consuming or dependent of parameter estimation. We developed a physics informed neural network with divergence-free vector potential as a non-parametric denoising technique for 4D flow MRI. Results from simulated pulsatile flow and CFD vascular model shows significant noise reduction and aliasing correction. Future work includes comparison with other techniques on different types of data and uncertainty quantification.

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