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AI-enabled Imaging, Emerging Imaging

High-risk coronary plaque imaging with super-resolution multi-parametric MRI

Project ID: 2021_024

1st Supervisor: Rene Botnar, King’s College London
2nd Supervisor: Claudia Prieto, King’s College London
3rd Supervisor: Julia Schnabel, King’s College London
Clinical Champion: Amedeo Chiribiri, King’s College London

Aim of the PhD Project:

  • To devise a deep-learning (DL)-based super-resolution reconstruction technique for ultra-high resolution (0.6mm3) coronary MR angiography.
  • To devise a super-resolution coronary vessel wall imaging sequence, which will enable coronary anatomy visualization (CMRA), IPH/thrombus visualization (black-blood PSIR) and coronary plaque burden (coronary vessel wall) quantification.
  • To devise a super-resolution model based joint T1/T2 mapping sequence of coronary wall integrated in above framework to enable quantitative coronary plaque characterization and high-risk plaque detection.

Project description/background:

Despite advances in treatment and prevention coronary artery disease (CAD) remains the leading cause of mortality in the Western world. Acute coronary syndromes are often the first manifestation of CAD and abrupt rupture of an unstable coronary artery atherosclerotic plaque (so called high-risk plaque) is the main instigator of myocardial infarction. The sequelae of plaque progression and destabilization is a complex and multifactorial process involving several biological processes including inflammation1, neovascularization, intraplaque hemorrhage (IPH)2, positive remodeling, extracellular matrix synthesis/degradation and microcalcification. In the era of precision medicine, a non-invasive and safe imaging modality for the early detection of these high-risk plaque features may enable improved risk stratification and targeted prophylactic treatment of patients at increased risk of developing ischemic cardiovascular disease and is therefore potentially of great clinical benefit.

Current non-invasive risk stratification strategies are based on population derived risk factors, with coronary computed tomography angiography (CCTA) often performed in individuals with low-intermediate cardiovascular risk to help guide early medical therapy3. Indeed, CCTA studies have indicated a relationship between stenosis, plaque burden and plaque composition (CT attenuation, positive remodelling and spotty calcification) with future cardiovascular events4. In addition, the recent CRISP-CT study5 demonstrated that higher perivascular fat attenuation index (FAI) on CCTA, a new measure for vascular inflammation, is associated with a higher risk of cardiac mortality beyond age, traditional risk factors, extent of CAD, and presence of high-risk plaque features. Even though these results collectively demonstrate the usefulness of CCTA for coronary risk assessment, CCTA is limited by radiation exposure and the need for iodinated contrast agents which limits repeated monitoring scans beyond the initial screening scan.

To address this limitation, we propose to develop a novel non-invasive, radiation-free and contrast-free Magnetic Resonance Imaging (MRI) framework for comprehensive assessment of coronary artery disease in a single multi-contrast and multi-parametric ultra-high-resolution 3D whole-heart scan. This approach will enable simultaneous assessment and automatic analysis of severity of coronary stenosis, total amount of plaque burden and features of high-risk plaque in one single radiation-free and contrast-free scan.


  1. Libby P. Nature. 2002;420:868-74.
  2. Kolodgie FD, et al. N Engl J Med. 2003;349:2316-25.
  3. Goff DC, Jr., et al. J Am Coll Cardiol. 2014;63:2935-2959.
  4. Motoyama S, et al. J Am Coll Cardiol. 2015;66:337-46.
  5. Oikonomou EK, et al. Lancet. 2018;392:929-939.


Schematic of project outline

Figure 1: Schematic of project outline

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