Description du projet
Combler le fossé technologique pour la reconstruction d’images de réseaux naturels
La reconstruction numérique précise de réseaux naturels tels que les vaisseaux sanguins ou les racines des plantes est cruciale pour garantir la qualité des prévisions basées sur la simulation. Ces structures ne sont toutefois souvent accessibles que par le biais de techniques d’imagerie non invasives, ce qui génère des artefacts qui compromettent la fiabilité des données et des simulations dérivées. Financé par le programme Actions Marie Skłodowska-Curie, le projet NIOT entend fournir les solutions mathématiques et technologiques requises pour reconstruire des réseaux à partir d’images corrompues. L’idée centrale est d’incorporer les avancées les plus récentes de la théorie du transport branché dans une méthode de traitement d’image variationnelle. L’ objectif ultime consiste à reconstruire les réseaux vasculaires corrompus dans les IRM de patients humains.
Objectif
"The precise digital reconstruction of natural networks such as blood vessels or plant roots is crucial to ensure the quality of
simulation-driven predictions. However, these structures can often be accessed only via noninvasive techniques, leading to artifacts
that compromise the reliability of the data and the derived simulations. No technological solution is currently able to recover digital
reconstructions of ""real"" networks from corrupted images.
The NIOT (Network Inpainting via Optimal Transport) project aims to fill this technological gap by defining for the first time a robust
mathematical formulation of the image network reconstruction problem. Thanks to the most recent advances of the optimal
transport theory, we will finally encode into equations the well-known fact that several natural networks are designed to transport
resources with the least effort possible. We will adopt a variational image processing method, where the reconstructed network is
obtained as the density minimizing the sum of the discrepancy with the observed data and a branch inducing functional. As such, our
proposed methodology builds a bridge between the image regularization and optimal transport communities.
A major ambition of the project is to pair the theoretical analysis with robust simulation tools that are capable of handling real data
arising from MRI acquisition techniques. This will require exploitation and development of dedicated components to handle large
datasets, both from a data handling and a multiscale simulation perspective. Our algorithm will be tested on a sequence of
increasingly channeling problems. We will start from simple synthetic networks, then we will use an high-quality map of the blood
vessel network of a mouse brain. The final benchmark will be to reconstruct of corrupted vascular networks in MRI scans of human
patients."
Mots‑clés
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Appel à propositions
(s’ouvre dans une nouvelle fenêtre) HORIZON-MSCA-2022-PF-01
Voir d’autres projets de cet appelRégime de financement
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinateur
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Norvège