Objective
Magnetic resonance imaging (MRI) is crucial to healthcare for its radiation-free, high-quality scans, but its high cost leaves 70% of the global population without access. Emerging low-field MRI technology offers affordable, portable systems with transformative potential, but faces critical challenges: long scan times, low signal-to-noise ratio (SNR), and poor tissue contrast, which makes some tissues indistinguishable. Although contrast agents help, they add risks. Early studies showed that low-field MRI can detect cancer without contrast agents using unique pulse sequences, but those are manually designed and slow. Recently, AI has been adopted for clinical high-field MRI, but AI pulse-sequence optimization relies on supervised learning, limiting discovery, AI theory is scarce, and simulations do not fully capture MRI’s complex spin dynamics. Moreover, AI use in low-field MRI remains mostly focused on image post-processing, while pulse sequence design, sampling, and reconstruction remain traditional and suboptimal.
I propose to develop a foundational AI framework that will transform low-field MRI into a rapid, high-quality modality. To break the barriers posed by supervised AI and simulations, my key innovation is an integrated AI-MRI framework, where on-the-fly MRI measurements guide a self-supervised AI search through the parameter space. Leveraging AI foundations I have recently developed, the framework will jointly optimize pulse sequences, sampling, and reconstruction to revolutionize imaging. Specific aims: (1) speed up MRI by an order of magnitude; (2) establish AI theory; (3) build the framework and develop cutting-edge sequences for optimal tissue contrast; and (4) demonstrate these in human scans with my lab’s low-field MRI. Preliminary results support the feasibility of our design aims. This project will transform low-field MRI into a fast, affordable, contrast-agent-free tool with broad clinical applications, particularly in low-income regions.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
32000 Haifa
Israel
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.