Reducing environmental impacts of CT scans with GenAI

My work has involved harnessing GenAI to support innovative advances in scanning techniques to support cancer management. Healthcare systems are facing the unprecedented challenge of caring for an ageing population. The European Cancer Information System reported 2.74 million new cancer diagnoses in 2022 and more than 3.24 million per year are anticipated by 2040. This places an increasing stress on the healthcare system to meet the growing demands for cancer management.

Computerised Tomography (CT) scans and Positron Emission Tomography (PET) scans are two of the key types of radiology imaging used for cancer patients around the world today. Contrast enhanced CT (CE-CT) scans rely on the injection of radiocontrast media (RCM) to produce detailed cross-sectional images of the body, while PET scans use radioactive tracers to visualise cancer activity within tissue. Typically, PET scans are combined with CT scans (PET-CT) to give anatomical and functional information that enhances the accuracy of diagnosis and enables decision-making about appropriate cancer treatment.

These types of CT scan require the injection of Radiographic Contrast Media (RCM) or radioactive tracers, and both are relatively resource intensive compared to non-contrast CT scans. They also have a far larger carbon footprint. The injection of radiopharmaceuticals for CE-CT and PET-CT scans requires the use of multiple single-use items, which have to be disposed of as clinical waste. On average, each CT scan generates 9kg of CO2 emissions, which is predominantly due to the contrast dye injection. The average CO2 associated with each PET-CT scan is 60kg. Altogether, the use of these RCMs in CT scanning accounts for about 3% of all pharmaceuticals detected in water systems.

To address some of these issues, my team invented and patented the method of synthesising ‘digital radioactive tracer’ and ‘digital contrast’, using through deep learning (DL) GenAI approaches. This has enabled us to remove the need for radiopharmaceuticals injection in CT scans. The first technical proofs of concept were developed in the context of aortic aneurysms and head/neck cancer, but subsequent work revealed the potential to apply this GenAI approach to simulating contrast enhancement in solid organs such as the spleen, and to identify abnormal/cancerous tissue in the liver and lung.

This ongoing research brings together a cross-disciplinary team working across four continents and 18 hospital sites. We are collaboratively establishing one of the largest CT data repositories (involving one million datasets) to support ongoing research and using GenAI to address the carbon footprint of CT scans.