IMC-PINN-FE estimates left-ventricular stiffness and active tension from images, then runs a physics-constrained neural FE simulation of the cardiac cycle about 75x faster than standard FE while matching imaged volumes.
Ontheusageofaveragehausdorff distance for segmentation performance assessment: hidden error when used for ranking
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
physics.med-ph 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
IMC-PINN-FE estimates left-ventricular stiffness and active tension from images, then runs a physics-constrained neural FE simulation of the cardiac cycle about 75x faster than standard FE while matching imaged volumes.