6 papers
Combining physics-based modeling and deep learning for ultrasound elastography
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Ultrasound elasticity images which enable the visualization of quantitative maps of tissue stiffness can be reconstructed by solving an inverse problem. Classical model-based appro…
Regularization by Adversarial Learning for Ultrasound Elasticity Imaging
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate ha…
MR elasticity reconstruction using statistical physical modeling and explicit data-driven denoising regularizer
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Elasticity image, visualizing the quantitative map of tissue stiffness, can be reconstructed by solving an inverse problem. Classical methods for magnetic resonance elastography (M…
Finite Element Reconstruction Of Stiffness Images In MR Elastography Using Statistical Physical Forward Modeling And Proximal Optimization Methods
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Quantitative characterization of tissue properties, known as elasticity imaging, can be cast as solving an ill-posed inverse problem. The finite element methods (FEMs) in magnetic…
Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priors
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of int…
A statistical framework for model-based inverse problems in ultrasound elastography
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Model-based computational elasticity imaging of tissues can be posed as solving an inverse problem over finite elements spanning the displacement image. As most existing quasi-stat…