Vascular ageing is characterised by the occurrence of alterations in the elastic laminae present in the media of elastic arteries such as the aorta. Imaging of the mouse aorta by very high-resolution X-ray micro-tomography using synchrotron radiation makes it possible to observe these structures in their context, in 3D and at exceptional resolutions and scales of detail. The MODELAGE project aims to develop innovative approaches adapted to the analysis of this type of atypical data. Thus, it will aim to characterise as precisely as possible the natural ageing of these arteries and their environment in healthy mice, to model normal ageing and describe the discrete vascular alterations that characterise it. This objectified knowledge base, built on observations of samples from mice of varying ages from very young to very old, will then be used to detect and characterise alterations occurring at comparable ages in diabetic mice to discriminate between those due to ageing itself and those that may be associated with the pathology. These results will finally be used to predict vascular behaviour both from the point of view of its possible ageing and its functionality. The MODELAGE project is a high-content biological image analysis project that will require the development of numerical methods and tools to meet the technological challenges associated with the mass and complexity of the data. Specific approaches to image analysis, machine learning and vascular simulation will take advantage of the latest high performance computing technologies.

Ischemic stroke is a major cause of disability and death worldwide. The induced severe disability cannot be reversed but with rapid treatment action. Recent clinical trials have demonstrated Endovascular Thrombectomy (EVT) to be highly effective, which has led to its widespread adoption in clinical routine. However, the conditions for a safe and efficient treatment are a strict management timeline and highly expert interventionalists. Training is a key element of performance and process improvement, and simulation plays an increasing and essential role, but there still lacks evidence that the commercial simulators can help improve clinical performance. In other words, the experience they convey is hardly transferable to the intervention room. PreSPIN (Predictive Simulation for the Planning of Interventional Neuroradiology procedures) aims at bridging the gap between training and intervention by addressing the planning phase. Here, numerical simulation can play a fundamental role on the condition that it is predictive, i.e. capable of rendering events that are critical to medical doctors. The project brings together a multidisciplinary team of researchers in numerical simulation, image processing and clinical medicine in order to tackle theoretical, technological and medical issues standing in the way towards a predictive planning system for the therapeutic management of acute ischemic stroke. The overall objective is to develop original methods to allow for high-fidelity and interactive simulation of catheter navigation and placement in the intracranial circulation, and real time synthesis of perfusion MRI images that takes advantage of an accurate simulation of blood flow in both the large vessels, as well as the capillary brain tissue. New blood vessel models will be designed, dedicated to the considered numerical simulations, and able to accurately capture the complex topology and geometry of the tortuous brain vasculature. In that context, PreSPIN will: • Objective 1: develop new solutions for segmenting and modeling the brain vasculature of the patient from MRA data, to provide geometric boundary conditions to forthcoming simulations; • Objective 2: propose new blood vessel models adapted to interactive simulation of interventional devices navigation, for a more informed choice of medical devices and treatment options; and fast blood flow simulation, to prevent potential risks associated with blood flow restoration; • Objective 3: progress in computational fluid dynamics (CFD) and investigate new and refined means of simulating perfusion imaging, to better predict treatment outcome. These advances will be supported by experimental data acquisitions with the aim of providing open-source in vitro data and reproducible setups to the community. Such data will be designed in close collaboration with interventional neuroradiologists who belong to the project’s partner teams, to both ensure critical phenomena are captured and make advances towards an engineered definition and assessment of medical predictivity. The project will have a definite impact on the clinical use of simulation for planning, but also for post-operative case review, which will constitute a situation of choice for our validation purpose. The results will directly be applicable to training and will enable a seamless integration of patient data in simulators, which is a major achievement to bring training experience closer to actual clinical conditions and make it transferable to the intervention. A longer term objective is to leverage simulation during the intervention to complement intra-operative data with unseen simulated information (e.g. blood pressure, device friction force, or full 3D view).

Giant congenital diaphragmatic hernia is a rare congenital malformation with a high morbidity and mortality rate even today despite the fabulous progress of neonatal resuscitation and antenatal follow-up. These advances have made possible to bring increasingly severe forms of hernia to surgery, requiring the development of new research and progress in the field of surgical repair. Currently, these hernias are treated surgically during the first week of life in specialized hospital and university centers by interposition of a prosthesis in the vast majority of cases. Almost 30% of these children will present a release of this prosthesis during their childhood significantly increasing the morbidity and mortality of this congenital malformation throughout the development of the child; this makes it a priority axis of the national rare disease plan with the designation of reference center (CHU de Lille, collaborator of this project) and competence centers (CHU Strasbourg, project leader). In recent years, we have been able to prove the link between recurrence by releasing the prosthesis and certain non-optimal biomechanical characteristics of prostheses currently used in hospitals: insufficient colonization and lack of neoangiogenesis covering these prostheses, mechanical properties unsuitable for growing organisms. The DIAPID project aims to develop new prostheses responding to this double challenge: - better adapted mechanical properties: better stretchability thus adapting to the growth of the child while maintaining good mechanical resistance thanks to the electrospinning process of materials meeting the requirements of a medical device) - and a monolithic bifacial structure with a dual objective: to limit the colonization of the implant by the host on the abdominal side of the prosthesis and to optimize this colonization and rapid neoangiogenesis on the thoracic side for lasting tissue integration. The encouraging preliminary results allow us to propose a design of prostheses with a fibrous and functionalized face on the thoracic side and optimal biomechanical characteristics theoretically limiting the risks of release during childhood and therefore the morbidity and mortality of this pathology. The first results in mechanical and biological analysis must be deepened by additional experimental tests ex vivo and then in vivo on large animals. The strength of this project lies in having been able to combine the skills of laboratories and researchers in complementary fields of expertise with, on the one hand, pediatric surgeons recognized for their expertise in the field of this rare disease, researchers specializing in electrospinning, in biomechanics, bioengineering, biological functionalization and finally the reference center for diaphragmatic hernias having developed an animal model of diaphragmatic hernia.

The digital twin, a virtual clone of a physical system, is seen as a key disruptive technology in the industry of the future. It is seen as a tool for steering Cyber-Physical Production Systems (CPPS) and twin should theoretically allow for improved commissioning, predictive maintenance and optimisation of the Production 4.0 system. Currently, the automation engineer uses simulation models of operating parts in a relatively empirical way for virtual commissioning and the implementation of different test scenarios in order to verify/correct the control part and thus save time during the real commissioning. Manufacturing engineers, for their part, use digital models for flow simulation, to evaluate decision-making scenarios (sequencing of operations, reconfiguration, impact of hazards, etc.). These simulation models are currently limited to testing the control system (once it has been completed) and to sizing and reconfiguring the installations, and are only used to a limited extent during the operating phase of the SCPP. The DT4CPS (Digital Twins for Cyber-Physical Systems) project aims to federate and enrich the simulation models used by automation and manufacturing engineers in the design phase, to make a digital twin. By ensuring coherence with the physical system throughout the life cycle (operation and improvement in particular), this digital twin should make it possible to improve the commissioning of SCPPs, such as reconfigurable manufacturing systems, and become a decision-making tool during the operation phase. To achieve this, the DT4CPS project aims to remove several scientific (command and control of SCPPs), methodological (what methods and tools for automaticians/manufacturing engineers/operators 4.0?), technical (interoperability of information systems) and human barriers.