Summary
My research uses Physics-Informed Machine Learning, Magnetic Resonance Imaging (MRI) and digital twins to help understand the human body and improve clinical treatments.
Some of the questions I am currently investigating are:
- Can we use Physics-Informed Machine Learning to simulate how the huma body works, and characterise its properties? Read our work on characterising cardiac properties Physics-Informed Neural Networks here.
- How do the atria deform? Can we use atrial mechanical information to understand cardiac disease better? Find the answers here.
- What clinical information can we extract from cardiac MRI with the help of machine learning? (Read our latest examples on automatically identifying aortic valve disease and quantifying fat around the heart.)
You may also be interested in our app to demonstrate how you may be able to treat cardiac arrhythmias (Android only).
Publications
Journals
Vimalesvaran K, Zaman S, Howard J, et al., 2024, Aortic stenosis assessment from the 3-chamber cine: ratio of balanced steady-state-free-precession (bSSFP) blood signal between the aorta and left ventricle predicts severity, Journal of Cardiovascular Magnetic Resonance, Vol:26, ISSN:1097-6647
Martin-Isla C, Campello VM, Izquierdo C, et al., 2023, Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge, Ieee Journal of Biomedical and Health Informatics, Vol:27, ISSN:2168-2194, Pages:3302-3313
Zaman S, Vimalesvaran K, Howard JP, et al., 2023, Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI, Journal of Medical Artificial Intelligence, Vol:6, ISSN:2617-2496
Lalande A, Chen Z, Pommier T, et al., 2022, Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge, Medical Image Analysis, Vol:79, ISSN:1361-8415