ArticleStatistical atlases and computational models of the heart. STACOM (Workshop)2021
PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps.
Article in Statistical atlases and computational models of the heart. STACOM (Workshop), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From bits to bedside: entering the age of digital twins in cardiac electrophysiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Review
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
- Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.Scientific reports · 2022Article
- Manifold Approximating Graph Interpolation of Cardiac Local Activation Time.IEEE transactions on bio-medical engineering · 2022Article
- Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate.Medical & biological engineering & computing · 2022Review
- Commentary: EP-PINNs: Cardiac electrophysiology characterisation using physics-informed neural networks.Frontiers in cardiovascular medicine · 2022Article
- Applications of multimodality imaging for left atrial catheter ablation.European heart journal. Cardiovascular Imaging · 2021Review
- GEASI: Geodesic-based earliest activation sites identification in cardiac models.International journal for numerical methods in biomedical engineering · 2021Article
- Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2021Article
- Deep Learning Framework for Real-Time Estimation ofFrontiers in physiology · 2021Article
- EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies.
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Registered trials
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