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| 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 |
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.
Technische Universiteit Eindhoven
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