What first made you interested in cardiology?
For someone whose favourite subjects at school were biology, maths and physics, medicine was a logical choice to combine these different interests. My fascination for cardiology began during my medical and specialist training at the University of Würzburg, Germany – steered by inspirational teachers, including Kurt Kochsiek and Georg Ertl – and grew throughout my post-doctoral research at Harvard Medical School, Boston, USA, under the cardiac metabolism innovator, Joanne Ingwall. The University of Würzburg has a great tradition in imaging, starting with the discovery of the X-rays by Röntgen in 1895, and during my time there, I was lucky enough to be able to work with one of the pioneers in the field of MRI, Axel Haase. So, it would seem that the stars were aligned for a career in cardiology and imaging, which would eventually take me to Oxford.
What are the key themes of your lecture?
My lecture will discuss new developments in cardiac MRI and CT. These technologies have had an enormous impact on cardiac imaging over the past 20 years and are now very much part of our routine diagnostic armamentarium. And yet we have probably only seen the very beginnings of their true capabilities. I will cover some of the latest advances that I think have the potential to change clinical practice in the coming years. These include an artificial intelligence (AI)-based method to replace late gadolinium enhancement MRI, 4D flow imaging for novel physiological biomarkers of CV risk, metabolic imaging to assess the mechanisms of new CV drugs, large-scale population imaging and a new CT method to non-invasively detect coronary inflammation, which is the main mechanism driving coronary plaque rupture and myocardial infarction risk.
What are the current challenges in your field?
A key challenge for MRI and CT imaging is to avoid, or at least further reduce, invasive imaging, and coronary CT is a prime example of success here. Another issue is to replace contrast-agent-based imaging with non-contrast methods and to avoid or reduce the radiation burden of imaging methods. Also, cardiac MRI is still relatively slow and speeding it up is a major goal. Similarly, there is a need to expedite analysis of MRI and CT imaging by supporting manual approaches with AI-based image-analysis tools. While there are technical and regulatory challenges to be overcome, significant progress is being made and we are clearly moving in this direction. Finally, in the current challenging economic climate, improving the cost-effectiveness of imaging is essential and many of the developments mentioned in my lecture will make significant contributions towards this.
Where do you think research in your field is heading in the future?
Important future research involves developing precision medicine – the tailoring of diagnosis, risk assessment and treatment to the individual patient rather than to a disease label. It is also important that we identify optimal ways to avoid CVD – we have made considerable efforts to develop diagnostic tools and treatments for established CVD, but have not invested as much in trying to prevent it. In addition, there is a need to identify powerful surrogate measures of clinical endpoints that will allow us to reduce the time and costs associated with hard endpoint-based trials, which currently require thousands of patients, years of follow-up and are very costly.
The novel imaging tools I will be talking about could play a crucial role in all three of these key areas for further development.