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ADAMI

Financiado

A Data-driven Approach to Microstructural Imaging

The ability to study tissue microstructure in vivo and completely noninvasively using magnetic resonance imaging (MRI) has the potential to radically change how we detect, monitor, and treat diseases, in particular the many neurod... ver más

30/04/2029

UANTWERPEN

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITEIT ANTWERPEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-02-14

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2023-COG: ERC CONSOLIDATOR GRANTS

I+D

Cerrada hace 1 año

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1 Participantes

UANTWERPEN

2.00M€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 62 meses Fecha Inicio: 2024-02-14
Fecha Fin: 2029-04-30

Líder del proyecto

UNIVERSITEIT ANTWERPEN

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto The ability to study tissue microstructure in vivo and completely noninvasively using magnetic resonance imaging (MRI) has the potential to radically change how we detect, monitor, and treat diseases, in particular the many neurodegenerative diseases that affect our world’s aging population. Unfortunately, the MRI signal is a very indirect measure of microstructure, and the variety of contributing factors complicates a one-to-one association between the MRI measurements and the biological substrate. As a result, microstructural mapping is still a poorly understood and challenging inverse problem that often yields inconsistent and contradictory outcomes. In ADAMI, I will take the next leap in microstructure imaging by approaching the problem in a completely data-driven fashion as opposed to the state-of-the-art that is model-driven. This paradigm shift will enable me to turn the MRI scanner into a powerful in vivo microscope that can provide reliable information about tissue microstructure that closely matches the underlying cellular composition. Rather than relying only on a single source of contrast, I will exploit the versatility of MRI and use multiple, independent contrast mechanisms that will provide the necessary information to distinguish reliably between microscopic substrates. Rather than relying on preconceived models, I will use machine learning to learn the appropriate models directly from the data. Rather than performing a posteriori histological validation of these new microstructural models, I will acquire a priori histological data to directly inform this learning process, guaranteeing, for the first time, a close match between microstructural readouts obtained from MRI and invasive histology. Through these innovations, ADAMI will advance the field of medical imaging by introducing a groundbreaking data-driven approach to microstructure imaging which will significantly impact the understanding, diagnosis, and monitoring of brain diseases and beyond.

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