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Found 8 papers, 2 papers with code
CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions
Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI).
Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data.
MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations
Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface.
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps
The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps.
Smoothness and continuity of cost functionals for ECG mismatch computation
The field of cardiac electrophysiology tries to abstract, describe and finally model the electrical characteristics of a heartbeat.
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation.
Efficient identification of myocardial material parameters and the stress-free reference configuration for patient-specific human heart models
Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning.
A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. realize such advanced applications methodological key challenges must be addressed.
