Abstract #3667
Prediction of peak-to-peak pressure gradient in patients with aortic coarctation using PINNs from Magnetic Resonance Images
Sebastian Jara1, Felipe Galarce2, Hernan Mella3, Ricardo Ñanculef4, Rodrigo Salas5,6, Francisco Sahli7,8, Philipp Beerbaum9, Heynric Grotenhuis10, David Marlevi11,12, Israel Valverde13, Sergio Uribe14, and Julio Sotelo15
1Universidad Técnica Federico Santa María, Valparaíso, Chile, 2School of Civil Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile, 3School of Electrical Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile, 4Departamento de Informática, Universidad Técnica Federico Santa Maria, Valparaíso, Chile, 5School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile, 6Millennium Institute for Intelligent Healthcare Engineering, iHEALTH., Santiago, Chile, 7Department of Mechanical and Metallurgical Engineering, Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 8Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, 9Department for Pediatric Cardiology and Intensive Care, Hannover Medical School, Hannover, Germany, 10Department of Cardiothoracic Surgery, UMC Utrecht, Utrecht, Utrecht, Netherlands, 11Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden, 12Institute for Medical Engineering and Science, Institute for Medical Engineering and Science, Cambridge, MA, United States, 13Division of Cardiology, The Hospital for Sick Children, Toronto, ON, Canada, 14Department of Medical Imaging and Radiation Sciences, Monash University, Melbourne, Australia, 15Departamento de Informática,, Universidad Técnica Federico Santa Maria, Valparaíso, Chile
Synopsis
Keywords: Flow, Diagnosis/Prediction, Aortic Coarctation, Pressure Gradient
Motivation: There is a need to find less invasive and more comfortable alternatives to interventional catheterization to assess the peak-to-peak pressure gradient (PGpp) in patients with aortic coarctation.
Goal(s): Demonstrate the use of physics-informed neural networks (PINN) to predict PGpp in patients with aortic coarctation.
Approach: The method uses cardiovascular magnetic resonance images to extract time series of cross-sectional areas and average blood flow velocity in the ascending and diaphragmatic aorta. Two PINN models are trained for these regions to predict the mean pressure, which are used to predict PGpp.
Results: PGpp predictions are in good agreement with those obtained by catheterization
Impact: This methodology demonstrates the potential of using PINN as a noninvasive alternative to catheterization to predict PGpp in patients with coarctation of the aorta. This method could significantly reduce the need for invasive procedures in the management of patients.
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