Project description
From sound waves to bubble dynamics
Diagnostic ultrasound uses high-frequency sound waves to produce internal images of the body. For cardiovascular diseases and cancer, imaging vasculature and flow is key to overcoming current hurdles in diagnosis and treatment. However, current clinical imaging modalities still provide insufficient spatiotemporal resolution. To address this issue, the ERC-funded Super-FALCON project will harness the nonlinear dynamics of novel contrast agents: monodisperse microbubbles. Using deep learning and GPU-accelerated simulations, it will recover super-resolved bubble cloud images. The project will also elaborate a new model for confined bubbles and use them as nonlinear sensors for capillary imaging. The goal is to produce fundamental knowledge about confined bubble dynamics, inhomogeneous ultrasound propagation and deconvolution strategies. Super-FALCON could initiate a paradigm shift towards patient-specific treatment.
Objective
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.
Fields of science
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- medical and health sciencesclinical medicinecardiologycardiovascular diseases
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesphysical sciencesacousticsultrasound
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
ERC - Support for frontier research (ERC)Host institution
7522 NB Enschede
Netherlands