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Mechanical characterization of soft tissue in vivo by microstructural imaging and physics-informed neural networks: bridging the gap between biomechanics and clinical practice

CORDIS · observation · Publication date unknown

ge 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

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recordType

award

status

SIGNED

region

EU

value

9991449

unit

EUR