| Title: | Application of deep learning for the characterisation of motion patterns in the heart |
| Lead Institution: | University of Oxford |
| Principal investigator: | Professor Vicente Grau |
Application 40161
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
The heart requires a complex combination of mechanical, chemical and electrical phenomena to produce and sustain an efficient beating action. Motion of cardiac walls during contraction and subsequent relaxation can be captured by modern imaging methods such as Cardiac Magnetic Resonance. It is known that such motion patterns are affected by disease, but current quantification methods leave substantial room for improvement. Machine learning, and in particular the more recent methods known as deep learning, has revolutionised several areas of research such as computer vision. Its effective use requires the availability of large data sets, which had been lacking until the appearance of large data collection initiatives such as UK Biobank. The proposed research aims at developing deep learning methods for the analysis of motion patterns in hearts from the UK Biobank, learning models of normal motion as well as the natural variability in the Biobank cohort, establishing links between these models and different subject characteristics, and eventually allowing the prediction of cardiac risks. The proposed research plan will be carried out initially over a 36 month period, with the expectation that further research questions are developed over that time leading to subsequent scientific questions and potential new projects.5 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 10425 | A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices | Abhirup Banerjee (+7) | 2021 | Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences |
| 9853 | Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks | Marcel Beetz (+2) | 2024 | IEEE Journal of Biomedical and Health Informatics |
| 7415 | Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology | Marcel Beetz (+2) | 2022 | Frontiers in Physiology |
| 9481 | Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images | Marcel Beetz (+3) | 2023 | Medical Image Analysis |
| 12853 | Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference | Lei Li (+6) | 2024 | IEEE Transactions on Medical Imaging |
Enabling scientific discoveries that improve human health