“Electrical Engineering and Information Technology” PhD Programme at KIT
Expected Results: We will provide an algorithm to quantify AF vulnerability for a given multi-scale model considering cellular and tissue (fibrosis) electrophysiological properties as well as the individual anatomy. We will provide a reentrant reaction-Eikonal scheme. These computer simulations will provide also evidences of the link between fibrotic markers and ablation outcomes.
Objectives: Regions of fibrotic tissue have been identified as a major contributing factor to AF, and we will identify which patterns of fibrosis will most likely lead to AF using computer modelling. We will investigate a) patches of various size, b) patches of various degree of fibrosis, c) several patches with various distance, d) patches with various degree of inhomogeneity of fibrosis, e) endocardial, epicardial and transmural patches of fibrosis. The fibrotic tissue will be implemented using a realistic 3D-model of the atria. A robust and comprehensive measure of arrhythmogenicity will be used to group the models in classes of high, medium und low arrhythmogenicity. To be able to thoroughly sample the high-dimensional parameter space, a reentrant reaction-Eikonal model will be formulated and solved by a novel numerical scheme. For the first time, this will allow to simulate fibrillation dynamics close to real-time.
Planned secondments: 3; ADAS3D, identify fibrosis characteristics from MRI images, ; Institut d’Investigacions Biomèdiques August Pi I Sunyer, identify clinically relevant patterns of fibrosis, Maastricht University mathematical modelling of endo-, epicardial and transmural extent of fibrosis
About the Project
Atrial Fibrillation (AF) is the most common cardiac arrhythmia affecting more than 6 million Europeans with a cost exceeding 1% of the EU health care system budget (13.5 billion annually). New treatment strategies and the progress achieved in research on AF mechanisms and substrate evaluation methods to date have not been commensurate with an equivalent development of the knowledge and technologies required to individually characterize each patient in search of the most efficient therapy.
PersonalizeAF addresses this challenge by delivering an innovative multinational, multi-sectorial, and multidisciplinary research and training programme in new technologies and novel strategies for individualized characterization of AF substrate to and increase treatments’ efficiency.
From the research point of view, PersonalizeAF will integrate data and knowledge from in-vitro, in silico, ex vivo and in vivo animal and human models to: 1) generate an individual description of the state of the atrial muscle identifying the disease mechanisms and characteristics; 2) understanding the potential effect that different therapies have on different atrial substrates; and 3) combining this information to generate a specific profile of the patient and the best therapy for each patient.
With this purpose, PersonalizeAF partnership aggregates relevant scientific staff from the academic and clinical world with highly specialised biomedical companies which will be involved in a high-level personalised training programme that will train a new generation of highly skilled professionals and guarantee ESRs and future PhD students outstanding Career Opportunities in the biomedical engineering, cardiology services and medical devices sectors. PersonalizeAF will disseminate results to a wide spectrum of stakeholders, create awareness in the general public about atrial fibrillation and encourage vocational careers among young students.
Please see https://personalizeaf.net/recruitment/
for detailed application instructions.