Project description
Advancing biomechanics for better diagnostics
Computational biomechanics holds great promise for improving patient care, but its full potential remains untapped. A key challenge is the lack of precise, patient-specific data on the mechanical properties of biological tissues. Current methods provide only partial insights. This hinders the integration of computational biomechanics into clinical practice. The ERC-funded MechVivo project aims to break this barrier by developing a revolutionary method to infer mechanical properties of soft tissues in vivo. Combining MRI, experimental biomechanics and physics-informed machine learning, the project will extract tissue properties from MRI scans and blood samples, paving the way for more accurate diagnoses (starting with heart failure with preserved ejection fraction).
Objective
Computational biomechanics is a fast-growing area of science. However, many of its fruits have not yet been translated into clinical practice. The main barrier to the translation of computational biomechanics into clinics is the lack of information about patient-specific mechanical properties of biological tissues. There are some methods for partially assessing these properties, but they have substantial limitations. A major cause of these limitations is the operating principle of current approaches, which rely on the analysis of the response of a tissue to some form of mechanical loading in vivo. To overcome this bottleneck, we propose a paradigm change. We will develop a method to infer mechanical properties of soft biological tissues in vivo based on a fundamentally new operating principle. To this end, we will leverage the synergy of three scientific areas covered by the three principal investigators of this project: (i) magnetic resonance imaging (MRI), (ii) experimental biomechanics, and (iii) physics-informed machine learning. Specifically, we will develop a new type of subvoxel MRI relaxometry to probe tissue microstructure non-invasively and establish a combined experimental and computational framework that will uncover for the first time the mechanistic link between transcriptomics, microstructure, and mechanical properties of soft biological tissues in a detailed manner. By leveraging this information with novel physics-informed machine learning techniques, we will gain the ability to determine the mechanical properties of soft tissues from clinical MRI data and blood samples with unprecedented accuracy and completeness. Our approach will be a crucial steppingstone to translate biomechanical computational models into clinical practice at a large scale. As a proof of concept, we will demonstrate how our new method can support the diagnosis of heart failure with preserved ejection fraction (HFpEF), one of the most common causes of mortality and morbidity.
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.
- natural sciences biological sciences biophysics
- engineering and technology medical engineering diagnostic imaging magnetic resonance imaging
- medical and health sciences clinical medicine cardiology
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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-SYG - HORIZON ERC Synergy 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-2024-SyG
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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.
21502 Geesthacht
Germany
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