Stroke and heart attack are the two most common cardiovascular diseases mainly due to rupture of atherosclerotic plaques and aneurysms. They are the No.1 killer and the first cause of disability in the world. Currently lesion dimension, e.g., the degree of luminal stenosis caused by the atherosclerotic plaque and the maximum diameter of an aneurysm, is the only validated criterion for risk assessment. However serious limitations exist in identifying higher risk patients based on this criterion, e.g., most symptomatic patients have lesions with dimension under the pre-defined threshold and this criterion cannot be used to assess the risk for asymptomatic patients accurately.
In vivo Imaging
Advanced imaging techniques, including high resolution magnetic resonance imaging (hrMRI), virtual histology intravascular ultrasound (VH-IVUS) and optical coherence tomography (OCT), allow the visualisation of detailed lesion morphological and compositional features that have been shown to provide significant incremental value to the lesion dimension for the vulnerability assessment. However, the predicting power is still insufficient for the prevention purpose. Additional non-invasive biomarkers are therefore needed if patient outcomes are to be improved. In addition to the lesion structural imaging, a systemic approach has adopted to examine the interactions between arterial disease and cardiac function both within the systemic and the pulmonary circulation based on CT and MRI.
Under the physiological condition, a lesion is subject to mechanical loading due to pulsatile blood pressure. The structure possibly fails if this loading exceeds its material strength. Therefore, critical mechanical conditions should be integrated with lesion compositional and compositional features for a more accurate vulnerability assessment.
The overall lesion geometry, location and patient presentation are similar, each, however, with high-dimensional image data and mechanical profile. The power of artificial intelligence (AI) is its ability to find patterns in complex data. The algorithms not only can find many of the same patterns as humans, but they detect sub-visual patterns that humans can’t see. Given thousands features from radiomics and mechanomics, advanced machine learning algorithms have the advantage of integrating the high-dimensional features from multiple modalities.
The technologies in our research include in vivo imaging, material testing, computational analysis and artificial intelligence, e.g., machine learning and deep learning. Imaging modalities used include, Ultrasound, MRI, CT, PET, DSA, VH-IVUS and OCT.
Material properties of arterial/atherosclerotic/aneurysm tissues are essential for assessing the stability of a lesion and for an accurate mechanical prediction. Ex vivo material test is often performed to understand the non-linear material behaviour of different atherosclerotic tissues and the ultimate material strength with the local micro structure and inflammatory condition.
Image Processing and Geometry Reconstruction
Various AI algorithms have been developed and are under developing to segment artery and associated lesions, e.g., atherosclerosis and aneurysm.
Finite Element Analysis
Various computational approaches have been employed to deal with solid-solid interaction and fluid-structure interaction problems in the artery with or without lesion or implantation inclusions. The computational analyses are mainly for the calculation of mechanical stress within the lesion structure or implantation.
These researches are funded by:
- ArTreat (European Research Council)
- British Heart Foundation
- Heart Research UK
- Medical Research Council
- Engineering Physical Sciences Research Council
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