Research lines

  • Deformation and function analysis in different cardiovascular diseases

    Current imaging devices provide the opportunity to measure the deformation of the heart while beating. However, interpretation of deformation and using it in clinical decision making is challenging and requires further processing and incorporation of knowledge of physiology and mechanics of the heart. We provide tools for this integrated assessment of cardiac deformation and relate it to function for clinical studies.

  • Quantification of structural and shape remodelling of the heart

    While some cardiovascular disease results in clear changes in the shape and structure of the heart (such as myocardial infarctions or congenital heart disease), other result in more subtle changes. This line investigates the 3D shape and structure quantification in e.g. athletes, low birth weight…

  • Machine learning for clinical diagnosis and decision making

    Contemporary artificial intelligence and machine learning tools allow to process large amounts of data from large groups of patients. While this approach has shown to be successful in image quantification, it is more challenging to extrapolate this to integrated diagnosis and clinical decision making. This line focusses on interpretable machine learning for assessing cardiovascular disease and risks from fetus to adult.

  • Synchrotron-based X-ray phase-contrast imaging

    In order to be able to visualise and quantify detailed knowledge about (rare of complex) structural abnormalities of the heart, current imaging does not always provide enough detail. Therefore, we are developing new imaging using synchrotron radiation to study the microstructure of the heart.

  • Computational modelling of the cardiovascular system

    While imaging provides a wealth of information, there are still many parameters relevant to the heart and its working that cannot be measured directly. Also, it is often difficult to isolate individual factors contributing to deterioration of cardiac function. Computational models capturing the cardiovascular mechanics and haemodynamics can help. These in-silico approaches are complementary to in-vitro and in-vivo research.

  • Cloud-based tools for data integration and analysis

    In order to structure relevant data for research and population analysis, and to ensure compatibility with new (computing) tools and equipment, a flexible software environment is required. We are developing a cloud-based platform for the integration and visualisation of imaging and clinical information and to flexibly use browser-based analysis to ease translation into clinical practice.