Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging

Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis

Applications of the golden angle in cardiovascular MRI

The use of radial trajectories has been seen as a potential solution to highly efficient cardiovascular magnetic resonance imaging (MRI). By acquiring a broad range of spatial frequencies per repetition time, the acquisition is time-efficient and robust against motion. Of particular interest is the golden angle profile order, which promises a near-uniform k-space coverage for an arbitrary number of readouts, enabling flexible data resorting, which is critical for efficient cardiovascular MRI. In Study I the use of 2D golden angle profile ordering is explored for imaging pulmonary embolisms. The insensitivity to motion and flow is used to reduce the artifacts that otherwise degrade images of the pulmonary vasculature when imaging with thin slices. It was found that the proposed technique could improve the image quality. Another source of artifacts arises when gradients are rapidly switched, and local induction of eddy currents may perturb spin equilibrium. In Study II, we propose a generalized golden angle profile orderings in 3D which reduces eddy-current artifacts. We demonstrate the efficacy of our generalization through numerical simulations, phantom imaging and imaging of a healthy volunteer. In Study III an improved 2D golden angle profile ordering was explored which resulted in a higher degree of k-space uniformity after physiological binning. This novel profile ordering was used in combination with a phase-contrast readout to enable quantification of myocardial tissue velocity and transmitral blood flow velocity, which are essential parameters for diastolic function assessment. When compared to echocardiography, it was found that MRI could accurately quantify myocardial tissue velocity, whereas transmitral blood flow velocity was underestimated. Study IV explored a further development of Study III by proposing a 3D version of the improved profile ordering. This novel ordering was used to acquire whole-heart functional images during free-breathing in less than one minute. Together, these results indicate that golden-angle-based imaging has the potential to improve cardiovascular MRI in several areas