Electromechanical Modelling of the Right Ventricle: the Forgotten Chamber
Despite AI important success in the recent years, its limited robustness to variations in input data makes it challenging to apply in healthcare. One reason is the lack of prior knowledge on human anatomy and physiology. Biophysical modelling is a mathematical framework to describe physiology which can encode prior medical knowledge. Electromechanical modelling of the heart has been an active research area in the last decades, however most of the focus has been on the left ventricle, while the right ventricle has been mostly ignored.
Right ventricular (RV) function evaluation is of utmost importance in heart failure, congenital heart disease, pulmonary arterial hypertension, pulmonary embolism, and most of respiratory diseases. This project is at the frontier between applied mathematics, computer and data science, and cardiology. Over the last 20 years, Dr. Maxime Sermesant at Inria has developed state-of-the-art mathematical models of the myocardium, as well as methods to personalise such models to clinical data for diagnosis and therapy planning. Dr. Maria A. Zuluaga, at EURECOM, has a large expertise in the development of learning-based techniques for cardiovascular image analysis, with state-of-the-art methods for the extraction of different cardiovascular structures. At Nice University Hospital, Dr. Pamela Moceri has developed an expertise in the clinical evaluation of the right ventricle, with state-of-the-art tools for detailed analysis of the RV and numerous clinical publications.
This project is international with a secondment at UPF in Barcelona with Pr. Bart Bijnens, a renowned researcher in cardiac echography and physiology, with a special interest in the right ventricle. UPF developed a detailed model of the RV fibrous structure based on synchrotron imaging, which impact on simulations started to be explored.
It is also in collaboration with the company Philips Healthcare in Paris through Dr. Mathieu de Craene, doing research on the analysis of cardiac shape and motion. It will enable privileged access to state of the art commercial tools. Interactions with industrial researchers will demonstrate how the tools developed could be integrated in future products.
Healthcare and biomedical engineering have one of the strongest recruitment increase in the last years, and skills acquired through this project will position well the fellow for his career. This project will utilise computational and data science approaches in healthcare, which is a research area with an important growth. The interactions with academic and industrial partners will ensure employability in these two sectors.
Medical imaging companies are currently developing new tools for shape and deformation analysis of the right ventricle. Such modelling approach is very complementary and could extend the possibilities of such products. Therefore there is an important potential for technology transfer. Finally, the Digital Twin concept which aims at creating a digital version of a patient to help diagnosis and therapy planning is currently promoted by large healthcare companies (Philips, Siemens,...). This electromechanical modelling project is perfectly in line with this concept, and should be of interest to these companies.
The project will follow a natural evolution of mathematical modelling of the right ventricle, starting from the shape and structure then moving to electrophysiological and biomechanical modelling. We will rely on a learning-based framework. This will be achieved in conjunction with the analysis of the corresponding clinical data available. In the later stages of the PhD, this will be applied to selected pathologies. Here is an outline of the PhD timeline:
1. RV shape (6 PM): learning-based statistical shape analysis of the RV to build a template mesh
2. RV structure (3PM + 3 PM Secondment): data analysis and model for a template fibre architecture
3. RV electrical activation (6PM): statistical analysis of activation maps for template electrophysiology
4. RV deformation (6PM): statistical analysis of RV strain to adjust biomechanical model
5. RV mechanical contraction (6PM): contractile RV function estimation for personalised predictions
6. RV pathologies: mechanisms and predictions (3PM + 3PM Secondment): selection of pathologies where clinical data enable personalised simulations and predictions
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