Cardiovascular magnetic resonance myocardial tissue characterisation

Abstract

Aortic stenosis affects over 5% of the elderly population and is increasing in prevalence. In this thesis, I assessed the long-term prognostic significance of myocardial replacement fibrosis as detected by late gadolinium enhancement cardiovascular magnetic resonance imaging (CMR) in patients with moderate and severe aortic stenosis. I further assessed the prognostic significance in patients who underwent aortic valve replacement. Although late gadolinium enhancement can detect myocardial replacement fibrosis, it does not allow identification of the more diffuse pattern of interstitial fibrosis, an earlier and potentially reversible form of fibrosis. New single breath-hold CMR techniques utilising MOdified Look-Locker inversion recovery (MOLLI) sequences have been developed, however they lack validation and quality assurance. I performed phantom work for quality assurance and after testing the sequence, undertook histological validation using intraoperative myocardial biopsies taken from patients with aortic stenosis. Biomarkers are likely to play an important role in risk stratification for patients with aortic stenosis. Lipoprotein(a) [Lp(a)] has been associated with aortic valve calcification and need for intervention, however the association with fibrosis remains unclear. I investigated the potential association between Lp(a) and fibrosis identified using CMR. Finally, utilising blood biomarkers to predict myocardial fibrosis can have both prognostic implications and a change in the way CMR resources are currently used. I developed a novel risk score for the prediction of myocardial fibrosis in patients with aortic stenosis. In conclusion, CMR myocardial tissue characterisation utilising the late enhancement method to identify replacement fibrosis provides valuable prognostic information in patients with aortic stenosis. Newer T1 mapping methods are able to reliably identify diffuse interstitial fibrosis and this may have incremental prognostic value. Future work may enable accurate risk stratification using a combination of blood and imaging biomarkers and help guide intervention in patients with aortic stenosis.