5 citations · 5 across the 4 of their papers we have counts for
4 papers
Characterising cardiac tissue properties with graph neural networks
Ching-En Chiu, Yoo Ri Kim, Magdi Saba +2
Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising a…
PINNing Cerebral Blood Flow: Analysis of Perfusion MRI in Infants using Physics-Informed Neural Networks
Christoforos Galazis, Ching-En Chiu, Tomoki Arichi +2
Arterial spin labeling (ASL) magnetic resonance imaging (MRI) enables cerebral perfusion measurement, which is crucial in detecting and managing neurological issues in infants born…
Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions
Ching-En Chiu, Aditi Roy, Sarah Cechnicka +6
Physics-Informed Neural Networks (PINNs) are fast becoming an important tool to solve differential equations rapidly and accurately, and to identify the systems parameters that bes…
Characterisation of Anti-Arrhythmic Drug Effects on Cardiac Electrophysiology using Physics-Informed Neural Networks
Ching-En Chiu, Arieh Levy Pinto, Rasheda A Chowdhury +2
The ability to accurately infer cardiac electrophysiological (EP) properties is key to improving arrhythmia diagnosis and treatment. In this work, we developed a physics-informed n…