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

Sector: Hospital • Location: Netherlands

Source: EU Funding & Tenders Portal

Project
Ongoing

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 sign

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The project “Next-gen ultrasound imaging by closing the perception-action loop” is an infrastructure initiative in the Hospital sector, located in Netherlands. Taiyo aggregates data on it from EU Funding & Tenders Portal.

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ongoing

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Description

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.

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100%

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