2 citations · 3 across the 8 of their papers we have counts for
8 papers
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Marcel Beetz, Abhirup Banerjee, Vicente Grau
Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture mo…
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Marcel Beetz, Abhirup Banerjee, Vicente Grau
Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation proce…
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Marcel Beetz, Abhirup Banerjee, Julius Ossenberg-Engels +1
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional…
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Marcel Beetz, Yilong Yang, Abhirup Banerjee +2
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. Howeve…
Influence of Myocardial Infarction on QRS Properties: A Simulation Study
Lei Li, Julia Camps, Zhinuo +4
The interplay between structural and electrical changes in the heart after myocardial infarction (MI) plays a key role in the initiation and maintenance of arrhythmia. The anatomic…
Multi-Modality Cardiac Image Computing: A Survey
Lei Li, Wangbin Ding, Liqun Huang +2
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and f…