SOURCE-LINKED INTELLIGENCE

Super-resolution, ultrafast and deeply-learned contrast ultrasound imaging of the vascular tree.

CORDIS · observation · Publication date unknown

safety, cost, and speed, and is thus the ideal candidate to address this need. The strategy I propose in Super-FALCON harnesses the nonlinear dynamics of monodisperse microbubbles. In WP1, I will use deep learning and GPU-accelerated acoustic simulations to recover super-resolved (1/20th of the wavelength) bubble clouds. In WP2, I will create a new model for confined bubbles, and use them as nonlinear sensors for capillary imaging. In WP3, I will disentangle attenuation and scattering using (physics-informed) deep learning and correct for wave distortion. This is needed to apply the strategies from WP1 and 2 in deep tissue. Finally, in WP4, I will use automatic segmentation to integrate the fundamental results of WP1, 2 and 3 into a technology that I will scientifically assess on vascularized ex vivo livers. With Super-FALCON, my ambition is to generate a long-term impact both scientifically and societally. I will produce new fundamental knowledge about confined bubble dynamics, inhomogeneous ultrasound propagation, and deconvolution strategies as well as new experimental methods for

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR