Physics-Informed Neural Operators for Tissue Elasticity Reconstruction

This publication has 16 references indexed in Scilit:

• Neural operators for accelerating scientific simulations and design
  Nature Reviews Physics, 2024
• Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography
  2023
• A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
  Computer Methods in Applied Mechanics and Engineering, 2022
• Physics-informed machine learning
  Nature Reviews Physics, 2021
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  Journal of Computational Physics, 2019
• Liver fibrosis: Review of current imaging and MRI quantification techniques
  Journal of Magnetic Resonance Imaging, 2016
• Magnetic resonance elastography as a method for the assessment of effective myocardial stiffness throughout the cardiac cycle
  Magnetic Resonance in Medicine, 2010
• MR-elastography reveals degradation of tissue integrity in multiple sclerosis
  NeuroImage, 2010
• MR elastography of breast lesions: Understanding the solid/liquid duality can improve the specificity of contrast‐enhanced MR mammography
  Magnetic Resonance in Medicine, 2007
• Magnetic resonance elastography: Non-invasive mapping of tissue elasticity
  Medical Image Analysis, 2001