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Super-FALCON

Financiado

Super-resolution, ultrafast and deeply-learned contrast ultrasound imaging of th...

Super-resolution, ultrafast and deeply-learned contrast ultrasound imaging of the vascular tree. Our healthcare system is under unsustainable strain owing, largely, to cardiovascular diseases and cancer. For both, imaging vasculature and flow precisely is paramount to reduce costs while improving diagnosis and treatment. Spec... ver más

31/03/2028

UNIVERSITEIT TWENT...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITEIT TWENTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-12-08

ERC-2022-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UNIVERSITEIT TWENTE

1.50M€ | Lider

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Duración del proyecto: 63 meses Fecha Inicio: 2022-12-08
Fecha Fin: 2028-03-31

Líder del proyecto

UNIVERSITEIT TWENTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Our healthcare system is under unsustainable strain owing, largely, to cardiovascular diseases and cancer. For both, imaging vasculature and flow precisely is paramount to reduce costs while improving diagnosis and treatment. Specifically, the focus is on the multiscale aspects of shear, vorticity, pressure and capillary bed (10-200 μm vessels) structure and mechanics. However, this requires an imaging depth of ~10 cm with a resolution of ~50μm. Furthermore, velocities often exceed 1m/s, which requires a frame rate of ~1000 fps. Clinical imaging modalities have so far been hindered by insufficient spatiotemporal resolution and there is thus a dire need for new techniques. Plane-wave ultrasound enhanced with contrast microbubbles outperforms all modalities in 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 flow imaging and characterization. In healthcare, Super-FALCON could initiate a paradigm shift towards patient-specific treatment.

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