Assessment of myocardial fibre structure in hypertrophic cardiomyopathy with magnetic resonance diffusion tensor imaging

Myocardial disarray is a likely focus for fatal ventricular arrhythmia in hypertrophic cardiomyopathy (HCM). Measuring the extent of this disarray could provide a better and more mechanistic marker of arrhythmic sudden cardiac death (SCD). Disarray may be inferred by using diffusion tensor cardiac magnetic resonance (DT-CMR), a novel non-invasive imaging technique which allows tissue microstructure to be assessed in vivo – something previously only possible in post-mortem specimens. DT-CMR probes the underlying myocardial architecture by characterising the three-dimensional process of water molecule diffusion as it is impeded by myocytes and interstitium. Thus, diffusion imaging surpasses the usual limits imposed by image resolution.
Disarray together with other structural and ionic HCM defects are likely to cause irregularities in myocardial depolarisation and repolarisation to produce an abnormal electrocardiogram (ECG). Computational techniques such as machine learning could objectively learn from and make predictions on HCM ECG morphology, to identify distinct phenotypes within this heterogenous disease. The overall aim of this thesis was to characterise the underlying HCM substrate using in vivo DT-CMR imaging and machine learning of high-fidelity digital ECGs.
Diastolic fractional anisotropy (FA), a parameter calculated from the diffusion tensor, was established as a marker of coherent myocyte alignment which demonstrated a mid-wall ring of high FA in healthy controls, corresponding to circumferentially-aligned mid-wall myocytes. In HCM, this ring was disrupted by reduced FA, despite the presence of normal helix angles which represents preserved transmural helical arrangement of myocytes. These findings are in concordance with published histology demonstrating that firstly, disarray and fibrosis invade circumferentially-aligned mid-wall myocytes at the interventricular insertion points and hypertrophied segment. And that secondly, the average myocyte orientation within the septal mid-wall remains circumferential despite disarray.
FA was reduced in HCM compared to controls. Reduced FA in HCM was due to disarray and fibrosis. Markers of fibrosis, late gadolinium enhancement (LGE) and extracellular volume imaging, were both significant predictors of FA, in line with fibrosis contributing to low FA. Yet FA adjusted for fibrosis remained reduced in HCM, suggesting that low FA is likely to represent disarray after accounting for fibrosis. Reduced FA was associated with ventricular arrhythmia in HCM, even after correcting for fibrosis and hypertrophy.
Phenotyping using machine learning of ECG morphology revealed four distinct HCM phenotypes. One phenotypic group with primary T wave inversion not secondary to QRS abnormalities had increased risk scores for SCD and increased extent of coexisting septal and apical hypertrophy compared to the other phenotypes.
In conclusion, DT-CMR can reveal the microarchitecture of the beating left ventricle. I propose that diastolic FA is the first in vivo marker of disarray. Computational ECG phenotyping captures abnormal anatomical and ionic mechanisms and provides insights into HCM heterogeneity. Both techniques may provide independent risk factors as current clinical risk predictors do not capture the underlying pathophysiology intrinsically linked to arrhythmic risk in HCM.

Advisor: Watkins, H
Neubauer, S
Robson, M