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Magnetic resonance images of the heart showing a comparison between standard gadolinium enhanced versus the new AI-enhanced images.

A cross-disciplinary team of artificial intelligence (AI) scientists, magnetic resonance specialists and cardiologists have developed a new cardiac magnetic resonance (CMR) scan for detecting heart muscle disease.

The current gold-standard for imaging heart muscle disease is CMR, using a method called late gadolinium enhancement. However, this requires injection of a contrast agent into the patient, which prolongs the scan, increases the cost, and is cautioned in some patients, such as those with severe kidney failure. Clearly, having a faster scan that provides the same information, but without the need for needles and contrast agents, would be very attractive to patients and doctors who need these scans.

A team at the Oxford Centre for Clinical Magnetic Resonance Research (OCMR) has developed an AI solution called ‘virtual native enhancement’. This combines MR images that do not normally need contrast injections, and uses AI to train machines to predict what a contrast-enhanced image would look like. This approach can produce images that are similar to traditional contrast-enhanced images, but without the need to inject the contrast dye.

The results, published in the journal Circulation, show that these AI-enhanced images can produce as clear or better quality images than the traditional contrast-enhanced images, providing doctors with the same information. This technique also does not need the expensive contrast agents, syringes and needles, and is therefore much faster and significantly cheaper than the conventional scans. With further development and testing on more heart conditions, it could lead to a next generation of CMR scans that are clearer, cheaper, safer, needle-free and more patient-friendly.

Dr Qiang Zhang, AI scientist at OCMR, and lead author on the study, said “We are excited by our concept of virtual native enhancement, where AI could serve as a virtual contrast dye to replace intravenous contrast dye. We aim to develop deep learning solutions at the frontier of clinical research for immediate patient benefit.”

Professor Stefan Piechnik, senior author and Head of Advanced Cardiovascular Image Processing at OCMR, said “The technique uses T1-mapping, a quantitative imaging of the heart. T1 mapping has been on forefront of cardiac imaging research for a number of years now, yet the standardisation and interpretation aspects have always been challenging for front-line doctors. This invention is a crucial stepping stone to the wide-spread use of CMR as a completely non-invasive scan, which retains all the image features that doctors have already become accustomed to through many years of practice.”

Professor Vanessa Ferreira, joint senior author and Deputy Clinical Director at OCMR, said “This new AI technology is a game changer for cardiac imaging. It is one big step closer to making cardiac MRI more accessible to patients and hospitals alike, not only for the NHS, but also more widely throughout the world. This may include lower to middle income countries due to the lower cost and shorter scan time for greater throughput.”

Professor Stefan Neubauer, Director of the OCMR, Head of Division of Cardiovascular Medicine and co-author, said “This is clearly an exciting development coming out of OCMR, and is expected to advance the field of CMR. The success of this technology continues a long legacy of effective inter-disciplinary collaboration that aims to bring high-impact, cutting-edge research from bench to bedside.”

 This work was undertaken in collaboration with researchers from University of Virginia, USA, Harvard Medical School and the Hypertrophic Cardiomyopathy Registry (HCMR) study investigators. The study was funded by the British Heart Foundation, the University of Oxford John Fell Fund, and the National Institute for Health Research Oxford Biomedical Research Centre.

Read the full paper. 

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