How artificial intelligence is shaping medical imaging
How artificial intelligence is shaping medical imaging

How artificial intelligence is shaping medical imaging

Dr Qiang Zhang of the Radcliffe Department of Medicine explains how artificial intelligence is being used to help researchers and physicians interpret medical imaging.

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.

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.

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Image: Development of VNE in detecting heart muscle scars for two different heart diseases. The right panels show our new contrast-free method, while the left panels show conventional contrast-enhanced which requires injecting contrast agents. Arrows point to the detected scars.

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.

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Image: Background of IV contrasts of MRI, and the emerging new era of AI “virtual contrasts”.

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.

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.

With thanks to Professor Stefan Piechnik and Professor Vanessa Ferreira, joint senior authors of the study.

Further reading in Circulation: Zhang et al 2021, Zhang and Burrage et al 2022.