8 papers
Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +3
Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point clouds. However, these methods are…
WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
Felipe Ãlvarez Barrientos, Tomás Banduc, Isabeau Sirven +1
The contractile motion of the heart is strongly determined by the distribution of the fibers that constitute cardiac tissue. Strain analysis informed with the orientation of fibers…
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Vahidullah Taç, Amirhossein Amiri-Hezaveh, Manuel K. Rausch +3
We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displ…
-PINNs: physics-informed neural networks on complex geometries
Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
Physics-informed neural networks (PINNs) have demonstrated promise in solving forward and inverse problems involving partial differential equations. Despite recent progress on expa…
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
EfraÃn Magaña, Francisco Sahli Costabal, Wernher Brevis
An important tool for experimental fluids mechanics research is Particle Image Velocimetry (PIV). Several robust methodologies have been proposed to perform the estimation of veloc…
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Veronika Spieker, Hannah Eichhorn, Wenqi Huang +9
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time…