- MRI scans are expensive, and even dangerous for some patients like pregnant women or children.
- A new AI-powered algorithm is helping abolish some challenges associated with medical scans, through a technology called 'virtual native enhancement'.
- This new artificial intelligence technology could slash the time that patients need to spend in an MRI scanner from the standard 30-45 minutes to 15 minutes.
- This can more than halve the scan cost while producing images that are clearer and easier to interpret.
Disruptive AI-based imaging technology might replace the injection of dye ‘contrast agents’ usually needed to show clear images of scar of the heart
Imagine you are a medical doctor, faced with a patient with suspected heart disease for symptoms such as chest pain, tightness, or shortness of breath. One way to find out what is happening, and help guide patient prognosis, is to do a cardiovascular MRI scan to look into any heart muscle abnormalities. The scan involves injecting a ‘contrast agent’ (a dye that will improve image contrast and show up scars on images) into a vein in the patient. Contrast-enhanced MRI has been the clinical standard to provide clear scar images, but it’s painful, and makes already expensive MRI scans even more so.
What’s more, this method is limited in patients with significant kidney failure – their kidneys have difficulty clearing the dye from their bodies, sometimes leading to irreversible complications. Some patients will be allergic to the contrast agent, and you might want to limit the use of injectable contrasts in some patients, such as pregnant women and children.
So how do you find out about what might be going on in your patient’s heart in that case, without injecting into them a contrast agent?
It turns out that injecting a contrast agent might not be the only way to get clear MR images to reveal scars in the heart muscle – in 2010, Professor Stefan Piechnik from the Radcliffe Department of Medicine at Oxford University came up with a method to study heart muscle properties, using a contrast-free MRI technique called T1-mapping. It produces an image of the heart with numerical values that change with different diseases.
Such contrast-free MRI contain a lot of information about heart tissue properties, some of which is subtle, or difficult to identify as a scar or other pathologies. As of now, researchers are still exploring the best ways to interpret and display the information from these contrast-free T1-maps, which is one of the reasons that they are not yet widely used by medical doctors.
How is the World Economic Forum bringing data-driven healthcare to life?
The application of “precision medicine” to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseases—and disease in general.
But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.
The World Economic Forum’s Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access – across borders – to sensitive health data for the treatment and diagnosis of rare diseases.
The data will be shared via a “federated data system”: a decentralized approach that allows different institutions to access each other’s data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.
The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).
This is why our cross-disciplinary team of AI scientists, magnetic resonance imaging specialists and cardiologists at the University of Oxford worked to find ways that artificial intelligence (AI) can enhance these contrast-free MRIs, to produce clear images of heart muscle scarring. AI effectively works like “virtual contrasts” to replace conventional intravenous contrasts.
We developed an AI-powered algorithm to combine multiple contrast-free MR images together with heart motion information, enhance the pathological signals in them, to reveal scars in a similar way to conventional contrast-enhanced MRI. This technology is called “virtual native enhancement”, or VNE, as it acts as an enhancer for the MR images, using only the ‘native’ (ie, non contrast agent enhanced) images produced by an MRI scanner.
In 2021, our team released the first proof of concept for this idea, by detecting scars in the heart muscle for patients with hypertrophic cardiomyopathy, a common genetic heart disease affecting 1 in 500 people, and the most common cause of cardiac death among young people.
Recently, we have found that VNE can also detect scars in patients who have had a heart attack. We compared contrast-free VNE with conventional contrast-enhanced MRI in these patients. We found that VNE highly agreed with the conventional MRI in detecting previous heart attack scars and their extent. Additionally, the VNE image quality was actually better, all without the patients needing to receive an injection.
Once completely validated, this new technology may slash the time that patients need to spend in an MRI scanner from the standard 30-45 minutes to within 15 minutes, saving more than half the scan cost, yet producing images that are clearer, more diagnostically useful, and easier to interpret.
We think that these successive breakthroughs mark the beginning of a new era of diagnostic medical imaging, using AI instead of IV contrasts to reveal pathologies in the human body: we might finally be able to get rid of injections when it comes to heart MR imaging.
We are now working to further improve the capabilities of this technology, to detect more complex heart diseases and their underlying mechanisms, beyond the diagnostic power of current MRI. We plan to use these methods in large clinical studies as a diagnostic tool for novel investigations.
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We think that this kind of Virtual Native Enhancement technology is an exciting and potentially game-changing advance for clinical MRIs in the future. Patients going in for a clinical MRI scan might not need an injection for most MRI scans, not just for the heart, but potentially for other organs as well. This would cut costs for healthcare providers, meaning that many more patients could access MRI scans; the risks of contrast-agent injections complications would disappear too. We hope adoption of this method could contribute to the digitalization of the NHS, something which is very much needed to address the backlog post COVID-19 pandemic.