Cardiovascular diseases (CVDs) are the main cause of illness and premature death in the EU. Personalized medicine and the use of simulations are the most promising areas of research with high potential benefits for patients, citizens and economy. The use of scientific computing tools in medical practice is, nevertheless, still in its infancy. The main causes are related to the requirement of elevated high-tech engineering know-how, the cost of computations and the poor numerical validation of tools against clinical studies. In this scenario, the MeDiTATe project aimed to deliver a comprehensive framework of simulation and imaging technologies, targeted at industrial and clinical-translation to accelerate the process of personalised cardiovascular medical procedures to ultimately improve patient care. The strength of the concept lies within the multi-disciplinary aspect given by the intense collaboration among different technical experts in the field of engineering, clinics, academics and industry. The core idea is to develop image based Medical Digital Twins of cardiovascular districts for a patient specific prevention and treatment of aneurysms and to make it available as "a service" for all in academia, hospital and industry.
The project involved 14 Individual Research Projects (IRP) defined across five research tracks:
1. High fidelity CAE multi-physics simulation with RBF mesh morphing (FEM, CFD, FSI, inverse FEM);
2. Real time interaction with the digital twin by Augmented Reality, Haptic Devices and Reduced Order Models;
3. HPC tools, including GPUs, and cloud-based paradigms for fast and automated CAE processing of clinical database;
4. Big Data management for population of patients imaging data and high-fidelity CAE twins;
5. Additive Manufacturing of physical mock-up for surgical planning and training to gain a comprehensive Industry 4.0 approach in a clinical scenario (Medicine 4.0).
The project has been successfully concluded, with 14 researchers completing their research paths and 10 obtaining PhD titles within 2024. Key outcomes of the projects include novel workflows for real-time hemodynamic assessment and aneurysm growth predictions, GPU-accelerated simulation tools, and uncertainty quantification models to enhance computational efficiency. The project also introduced advanced imaging techniques and auxetic stent graft designs for improved endovascular repair, mock loops for validating endovascular procedures, and non-invasive ultrasound-based diagnostics for assessing aneurysm mechanical properties.
