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Next-gen ultrasound imaging by closing the perception-action loop

Project description

Enhancing ultrasound image quality: beginning with the end in mind

Improving the image quality of low-cost ultrasound imaging would enhance access to diagnostics. Funded by the European Research Council, the US-ACT project sees ultrasound as a perception action device, the action being acquiring information about the human body and the perception being the reconstruction of that anatomy. The project plans to enhance image quality by closing the perception-action loop, using perception to drive acquisition. Leveraging mathematics and the concept of manifold dimensionality, the project will deliver strong perception models based on advanced deep generative networks. Using the pure anatomy described by the generative model, acquisition will identify the point on its low-dimensional manifold, the reduced data that retains the accuracy required for the task.

Objective

Ultrasound (US) can revolutionize and democratize medical imaging if it offers: (1) access for everyone, and (2) excellent Image Quality (IQ). MRI offers (2) but is expensive and will thus not likely be able to provide (1). Low-cost US hardware technology will enable (1) in the future but is not expected to yield the needed breakthrough for (2). Consequently, any paradigm-shifting advance in signal processing technology that achieves US with excellent IQ will have a huge impact.

I propose a conceptually new and highly-unconventional approach that I believe can lead to a new generation of US technologies with excellent IQ. I will formally describe US systems as intelligent autonomous agents that perform actions and perception using probabilistic inference: the action is the acquisition, probing the world, and the perception is the reconstruction that infers what anatomy most likely generated the acquired US data. I conclude that current US systems are in essence flawed agents since (1) actions are not driven by perception, i.e. the perception-action loop is broken, and (2) their generative perception models are naive. My proposal will address this by closing the perception-action loop and offering strong perception models based on advanced deep generative networks. This breaks a fundamental tenet in US imaging, where I put forth the important concept that the acquisition and perception should work together to identify the point on the low-dimensional manifold of pure anatomy (described by the generative model) that is being imaged.

My intelligent US agents will pursue excellent IQ under the heading of a single probabilistic principle: minimization of ``surprise’’ under the agent’s own prior belief (the generative model) that such high-quality images can indeed be achieved. With this, we open a new frontier within active imaging (in US and beyond) where data acquisition and information processing are treated jointly based on expressive generative density functions.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2022-STG

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Host institution

TECHNISCHE UNIVERSITEIT EINDHOVEN
Net EU contribution
€ 1 812 500,00
Address
GROENE LOPER 3
5612 AE Eindhoven
Netherlands
Region
Zuid-Nederland Noord-Brabant Zuidoost-Noord-Brabant
Activity type
Higher or Secondary Education Establishments
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Total cost
€ 1 812 500,00

Beneficiaries (1)