Imagine if a doctor could see the detailed internal images of a CT scan without needing to inject a patient with chemical dyes. This technology uses AI to 'paint' those details digitally onto a standard scan. It removes the need for needles and toxic chemicals, making the process safer for kidneys and better for the planet.
Traditional CT scans rely on iodinated contrast media that cause pharmaceutical pollution, risk patient kidney failure, and generate significant carbon emissions.
An AI software pipeline that synthesizes 'Digital Contrast' from non-contrast CT scans and a trusted repository of 590k+ CT datasets.
If you are a scanner manufacturer dealing with the high environmental cost of traditional imaging — this project developed AI software that synthesizes digital contrast. This allows your machines to provide high-quality images while reducing the 9.2 kg of CO2 generated per scan.
If you are a hospital group dealing with patient risks like kidney failure and allergic reactions from contrast media — this project developed a digital alternative. It eliminates the need for iodinated contrast, reducing medical waste and improving patient safety.
If you are a software provider dealing with the lack of high-quality, standardized medical data — this project developed a trusted CT image repository with over 590k datasets. This provides a scalable foundation for training and validating new medical AI tools.
Based on available project data, specific pricing is not provided, but the project aims to demonstrate economical impact through health technology assessments.
Yes, the project aims to reduce 30% of CO2e and iodine waste from contrast-enhanced CTs globally by 2033 using a scalable reference system.
Based on available project data, the project is establishing legal and ethical frameworks to ensure trustworthiness, but specific licensing terms are not listed.
The project aligns with the ethical principles for trustworthy AI set by the European Commission’s High-Level Expert Group on AI.
The project runs from December 2023 to November 2027, with a long-term goal of achieving waste reduction targets by 2033.
The project is heavily industry-driven with a 45% industry ratio, including 10 companies and 8 SMEs. This strong commercial presence, combined with 6 universities and 2 research centers across 12 countries, suggests a high likelihood of commercial translation and a focus on practical market application rather than pure theory.
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