1 citations · 3 across the 5 of their papers we have counts for
5 papers
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States
Siyu Mu, Wei Xuan Chan, Choon Hwai Yap
The unloaded cardiac geometry (i.e., the state of the heart devoid of luminal pressure) serves as a valuable zero-stress and zero-strain reference and is critical for personalized…
IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
Siyu Mu, Wei Xuan Chan, Choon Hwai Yap
Elucidating the biomechanical behavior of the myocardium is crucial for understanding cardiac physiology, but cannot be directly inferred from clinical imaging and typically requir…
Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning
Wenhao Ding, Choon Hwai Yap, Kangjun Ji +1
A generative model for the mesh geometry of intracranial aneurysms (IA) is crucial for training networks to predict blood flow forces in real time, which is a key factor affecting…
Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows
Hong Shen Wong, Wei Xuan Chan, Bing Huan Li +1
Fluid dynamics computations for tube-like geometries are important for biomedical evaluation of vascular and airway fluid dynamics. Physics-Informed Neural Networks (PINNs) have re…
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography
Md. Kamrul Hasan, Haobo Zhu, Guang Yang +1
Temporal echocardiography image registration is a basis for clinical quantifications such as cardiac motion estimation, myocardial strain assessments, and stroke volume quantificat…