Submitted by Elena Manea on Mon, 01/13/2025 - 13:36
Keywords:
HEMODYNAMIQUE
RMN DE FLUX
Exploring radial sampling strategies for fast and reliable flow measurements in MRI by combining realistic numerical simulations and advanced deep learning-based image reconstruction
Project Leader:
INSA Lyon
INSA’s scientific leader:
Bruno SIXOU
The goal of the project is to provide clinicians a fast and reliable, velocity quantification method to explore abnormal blood flow in the most anatomically
INSA Challenge:
Santé Globale et Bioingénierie
Partners:
UNIVERSITÉ DE MONTPELLIER
CNRS
Funding Institution:
ANR
Dates - Duration:
2024-10-01 00:00:00 to 2028-12-01 00:00:00
Funding:
470292
Contact:
bruno.sixou@insa-lyon.fr
Chapo:
Are numerical simulations and deep learning useful to improve the study of blood flow with Flow MRI?
Climatology of Chronic Non-ischemic Cardiomyopathy long term prediction from multi-scale data and models
Project Leader:
INRIA
INSA’s scientific leader:
Olivier BERNARD
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.
INSA Challenge:
Santé Globale et Bioingénierie
Partners:
INRIA
HCL
APHM MARSEILLE
Funding Institution:
PIA ANR
Dates - Duration:
2023-07-01 00:00:00 to 2027-08-01 00:00:00
Funding:
1796572
Contact:
olivier.bernard@insa-lyon.fr
Chapo:
Prediction of the evolution of non-ischemic chronic cardiomyopathies through computational models
Stratification du risque de l'embolie pulmonaire par modelisation de l'arbre vasculaire pulmonaire
Project Leader:
INSA LYON
INSA’s scientific leader:
Odyssée MERVEILLE
Pulmonary embolism (the blockage of a pulmonary artery by a blood clot) is the third cause of cardiovascular death in Europe. Upon diagnosis confirmation, clinicians evaluate the patient prognosis based on risk stratification models. The management of patient, thus its outcome, highly depend on this risk stratification. Recent studies confirmed that patient stratification based on computed tomography pulmonary angiogram (CTPA) are not well correlated to patient prognosis.
INSA Challenge:
Santé Globale et Bioingénierie
Funding Institution:
ANR
Dates - Duration:
2023-01-01 00:00:00 to 2026-12-01 00:00:00
Funding:
279046
Contact:
odysse.merveille@insa-lyon.fr
Chapo:
Prédiction du risque de patients atteints d’embolie aigue à partir de biomarqueurs fonctionels et morphologiques
Diagnostic étiologique de maladies cardiaques basé sur les images échocardiographiques et les données cliniques
Project Leader:
INSA LYON
INSA’s scientific leader:
Olivier BERNARD
Echocardiography plays a paramount role in day to day clinical practice to highlight structural or functional dysfunction of the heart. The observed abnormalities combined with clinical data of the patient lead to diagnoses, many of which requiring the performance of complementary examinations to diagnose the origin (etiology) of cardiac pathology which will modify the prognosis of patient and the subsequent therapy.
INSA Challenge:
Santé Globale et Bioingénierie
Partners:
CNAM
SORBONNE UNIVERSITE
CHU CAEN NORMANDIE
Funding Institution:
ANR
Dates - Duration:
2022-12-01 00:00:00 to 2026-11-01 00:00:00
Funding:
340762
Contact:
olivier.bernard@insa-lyon.fr
Chapo:
Prediction of the origin (etiology) of cardiac pathologies from echocardiographic image sequences and patient data
Imagerie hyperspectrale ultrarapide pour l'optique biomédicale
Project Leader:
INSA LYON
INSA’s scientific leader:
Nicolas DUCROS
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.
INSA Challenge:
Santé Globale et Bioingénierie
Partners:
UNIVERSITE CLAUDE BERNARD LYON 1
HCL
Funding Institution:
ANR
Dates - Duration:
2023-01-01 00:00:00 to 2026-06-01 00:00:00
Funding:
401602
Contact:
nicolas.ducros@insa-lyon.fr
Chapo:
The objective of this project is to design the next generation of fast spectral imagers for biomedical applications
Submitted by Anonyme (not verified) on Wed, 09/08/2021 - 16:47
Keywords:
IMAGERIE MEDICALE
ARTIFICIAL INTELLIGENCE
Project Leader:
UNIV DE BRETAGNE OCCIDENTAL
INSA’s scientific leader:
Bruno SIXOU
L’objectif de ce projet est de développer des nouvelles méthodes de reconstruction d’images médicale multimodales en utilisant des techniques d’intelligence artificielle.
Submitted by Anonyme (not verified) on Wed, 04/29/2020 - 14:02
Keywords:
APPRENTISSAGE PROFOND
SEGMENTATION DE VAISSEAUX SANGUINS
Régularisation par apprentissage profond - Application aux vaisseaux sanguins
Project Leader:
INSA LYON - CREATIS
INSA’s scientific leader:
Odyssée Merveille
Malgré le boom de l’intelligence artificielle (IA), cette dernière peine à s’imposer en imagerie médicale car elle requiert de larges bases de données souvent indisponibles.
Ce projet propose de mélanger des méthodes classiques (variationnelles) et de l’apprentissage profond (IA) en utilisant des images simulées permettant de contourner le manque crucial de données. L’application principale envisagée est la détection de vaisseaux sanguins pour une meilleure prise en charge des suites de l’AVC.
Submitted by Anonyme (not verified) on Thu, 11/21/2019 - 15:35
Simulation pour la prédiction de l'évolution des lésions dans l'accident vasculaire cérébral
Project Leader:
INSA LYON - CREATIS
INSA’s scientific leader:
Carole FRINDEL
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.