Non-ischemic chronic cardiomyopathies (NICM) represent a complex group of cardiac diseases, accounting for 40% of heart failure patients. The challenge in classification, diagnosis, and treatment stems from their multifactorial nature and clinical variability. Major subtypes include dilated and hypertrophic cardiomyopathies (DCM and HCM), each with distinct characteristics. These diseases can progress to heart failure and carry a risk of cardiac arrhythmias, including sudden death. Currently, there are no risk stratification scores for these patients.

We aim to develop a concept where physics, computer science and applied mathematics will enable faster imaging than with conventional techniques. To do so, we propose to shape light with spatial light modulators, which will allow the acquisition of multiple pixels at once and provide improved signal-to-noise ratios. Inspired by recent advances in artificial intelligence, we will develop neural networks capable of reconstructing high spectral resolution images in real time.

Cardiac valve diseases are known to be an important public-health problem. Mitral Valve (MV) regurgitation (MR), also known as mitral insufficiency, is one of the most important of them. The regurgitation is either caused by a pathology of the valve itself (primary MR), or it is the consequence of a pathology of the myocardium (secondary MR).

Ce projet propose de simuler des images avec un fort degré de réalisme physiologique dans le cadre de l'AVC, dans le but de créer des jeux de données suffisamment grands pour permettre aux approches d'apprentissage automatique d'être efficaces.

Pour ce faire, ce projet vise une simulation qui intègre la mécanique des fluides dans la lumière vasculaire spécifique au patient extrait à partir de données d'angiographie tridimensionnelle.

La détection des emboles est un sujet majeur en matière de prévention des AVC.