2023-05817 - Internship Deep-Learning model for vascular tree simulations
Contract type : Internship
Level of qualifications required : Bachelor's degree or equivalent
Other valued qualifications : Master of engineering
Fonction : Internship ResearchContext
The project is an ongoing collaboration between HeartFlow Inc (California, USA), Inria, Université Gustave-Eiffel and CentraleSupélec. Thus both the internship and the PhD will be co-supervised by a team of persons belonging to these institutions. Academically: Irene Vignon-Clementel, Laurent Najman and Hugues Talbot, and PhD student Raoul Salle de Chou.Assignment
Heart disease is a leading cause of morbidity and mortality in the world. The California-based company Heartflow is a leader in non-invasive, image-based, physically consistent models for blood flow into the myocardium. The basis of the model is the segmented arteries from CT scans, which form a vessel tree that feed blood into the myocardium. However, even with the highest resolution CT scan available in a clinical context, only a few branches can be recognized and segmented. This is not sufficient to understand how the blood coming from these arteries actually perfuse into the myocardium.
Objectives : The main scientific objectives of this internship are to develop a combined AI + numerical methods to extend the segmented vessel tree in a biophysics- consistent manner, and to simulate perfusion. This will be compared with actual perfusion maps acquired using an invasive, rarely available and expensive PET-based method
For a better knowledge of the proposed research subject :
1 Jaquet, Clara, et al. “Generation of Patient-Specific Cardiac Vascular Networks: A Hybrid Image-Based and Synthetic Geometric Model.” IEEE Transactions on Biomedical Engineering , 2019 https: // doi.org/10.1109/TBME.2018.2865667.
2 Papamanolis, Lazaros, et al. “Myocardial Perfusion Simulation for Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model.” Annals of Biomedical Engineering , 2021 https: // doi.org/10.1007/s10439-020-02681-z.
3 Odyssee Merveille, Benoıt Naegel, Hugues Talbot, and Nicolas Passat. N-d variational restoration of curvilinear structures with prior-based directional regularization. IEEE Transactions on Image Processing, 2019.
4 Mohammad Sarabian, Hessam Babaee, and Kaveh Laksari. Physics-informed neural networks for brain hemodynamic predictions using medical imaging. IEEE transactions on medical imaging, 2022.Main activities
Technical skills and level required : Python (advanced), pytorch and github is a plus.
Languages : English (fluent)Benefits package
The ideal candidate will have an engineering background with strong analytical skills and experience in developing Python scripts. Notions of mathematics in optimization and deep learning. Experience in modelling is a plus. Fluent English is a must.About Inria
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.Instruction to apply
Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.
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