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, a BHF CRE PI, 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 a 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.
"The Artificial Intelligence adds a crucial functionality to interpreting T1 maps previously available only to experts. With the clear progress in validation of VNE across the wide range of human heart conditions, we are getting ever so closer to a meaningful deployment of rapid and fully non-invasive cardiac MR at the frontline clinical practice."
Prof. Stefan Piechnik, joint senior author and BHF CRE PI
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.
"Deep learning has opened new ways to transform medical imaging and healthcare technologies in general. We work actively with clinicians in hospital settings to develop state-of-the-art deep learning algorithms, with the aim to advance clinical practice and improve patient care."
Dr Qiang Zhang, lead author
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.
Image: Development of VNE in detecting heart muscle scars for two different heart diseases. The bottom panels show our new contrast-free method, while the top panels show conventional contrast-enhanced method 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.
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.
"The Virtual Native Enhancement (VNE) technology is an exciting and potentially game-changing advancement in the field of MRI, where we may be able to eliminate the use of injectable contrast agents for most MRI scans in the future. This may have significant cost-savings implications for healthcare systems, and may open up the door for many more patients to access MRI by reducing cost and risks of complications relating to contrast injections."
Prof. Vanessa Ferreira, joint senior author and BHF CRE PI
This kind of Virtual Native Enhancement technology would significantly 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.