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Active HORIZON European Commission

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

€1.81M EUR

Funder European Commission
Recipient Organization Technische Universiteit Eindhoven
Country Netherlands
Start Date Apr 01, 2023
End Date Mar 31, 2028
Duration 1,826 days
Number of Grantees 1
Roles Coordinator
Data Source European Commission
Grant ID 101077368
Grant Description

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.

All Grantees

Technische Universiteit Eindhoven

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