Project description
A groundbreaking data-driven approach poised to transform tissue imaging
The ability to study tissue microstructure through magnetic resonance imaging (MRI) has long been a challenge, mainly marred by indirect measurements and inconsistent results. Funded by the European Research Council, the ADAMI project is shifting from the traditional model-driven approach to a data-driven methodology. The project will leverage machine learning and multiple MRI contrast mechanisms to develop models that mirror the actual cellular composition more accurately. The integration of direct histological data into the model learning process should help ensure a closer alignment between MRI-derived microstructural readouts and invasive histology. This novel strategy promises to transform MRI scanners into effective in vivo microscopes, offering new depths of insight into tissue microstructure.
Objective
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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
2000 Antwerpen
Belgium