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At the Inaugural Session, Professor Sven Plein, EACVI CMR Section Past Chair (2014-2016), will give the Keynote Lecture, entitled, ‘A brief history of CMR – and how to deal with uncertainty in medical imaging.’
Prof. Plein explained his own personal history with CMR: “As a medical student in Germany in the mid-1990s, I saw cine MR of the aorta for the first time and was fascinated by its clarity and detail. This image has remained in my mind ever since and has had a major influence on my career. It was around this time that the development of cardiovascular MRI accelerated. A few years later, I was fortunate to be offered a research fellowship in the British Heart Foundation-funded CMR unit in Leeds, which had one of the first dedicated CMR scanners in the world, giving me a unique opportunity to shape a career in this rapidly evolving field.
Over the past 20 years, I have seen first-hand the evolution of CMR from a niche research tool to a mainstream clinical technique. One of the main initial challenges to overcome in this development was to speed up image acquisition. As a young research fellow, my first project was to validate ‘real-time’ cine imaging methods. Later, we developed fast protocols for comprehensive CMR assessment of coronary artery disease (CAD) and applied these in large populations and randomised clinical studies, which have been taken up in clinical practice guidelines, where CMR now has a role in the assessment of CAD alongside other longer-established imaging methods.”
Prof. Plein discussed areas of uncertainty related to CMR. “As one example, many of my first research papers in CMR dealt with measurements of cardiac chamber sizes—a daily task in every CMR laboratory and one of the key ‘selling points’ of CMR. One would assume that the best strategy to measure chamber size was well established today. However, I have recently served on an expert panel that aimed to set standards for post processing of CMR images and we discussed many of the same issues I worked on 20 years ago as a young fellow, such as the best strategy for the placement of myocardial contours and inclusion criteria for the basal-most slice. Clearly, there is ongoing uncertainty over even this very simple CMR method. However, this has not hindered progress, because the medical community is well used to dealing with uncertainties, for example, by adopting institution-specific ways of interpreting images and accepting that small differences in measurements are physiologically normal. Today, with the rapid advent of artificial intelligence, we may enter a new era where automated analysis brings at least improved reproducibility of analysis across centres.
As another example, development of myocardial perfusion imaging has been rapid and the potential of CMR to assess myocardial perfusion not only visually but also quantitatively is now well established. As the field is moving from visual interpretation to automated modelling of quantitative blood flow assessment, it becomes clear how difficult it is to validate the measurements obtained. Clinical reference standards such as positron emission tomography and fractional flow reserve have their own limitations and inadequacies. Models and phantoms can only partially reproduce human physiology and for some measurements that can be derived from myocardial perfusion CMR analysis, such as the permeability of myocardial capillaries, there are no in vivo reference methods at all. One way of dealing with this uncertainty is to establish large datasets that determine clinically relevant prognostic thresholds as done for other imaging methods.”
“Constant advances in CMR methodologies allow us to gain new insight into the heart that we didn’t think possible just a few years before.”
Prof. Plein concludes, “What’s fascinating about CMR is that it’s development never stops and new methods emerge all the time. This leads to a degree of uncertainty as there is often a lack of ground truths to validate these methods against.”
Inaugural Session; Thursday 17:05 –18:05; Sala Grande
Our Mission: To Reduce the Burden of Cardiovascular Disease
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