  Coronary artery disease (CAD) remains the world's leading cause of mortality
and the disease burden is continually expanding as the population ages.
Recently, the MR-INFORM randomised trial has demonstrated that the management
of patients with stable CAD can be guided by stress perfusion cardiovascular
magnetic resonance (CMR) imaging and it is non-inferior to the using the
invasive reference standard of fractional flow reserve. The benefits of using
stress perfusion CMR include that it is non-invasive and significantly reduces
the number of unnecessary coronary revascularisations. As compared to other
ischaemia tests, it boasts a high spatial resolution and does not expose the
patient to ionising radiation. However, the main limitation of stress perfusion
CMR is that the diagnostic accuracy is highly dependent on the level of
training of the operator, resulting in the test only being performed routinely
in experienced tertiary centres. The clinical translation of stress perfusion
CMR would be greatly aided by a fully automated, user-independent, quantitative
evaluation of myocardial blood flow. This thesis presents major steps towards
this goal: robust motion correction, automated image processing, reliable
quantitative modelling, and thorough validation. The motion correction scheme
makes use of data decomposition techniques, such as robust principal component
analysis, to mitigate the difficulties in image registration caused by the
dynamic contrast enhancement. The motion corrected image series are input to a
processing pipeline which leverages the recent advances in image processing
facilitated by deep learning. The pipeline utilises convolutional neural
networks to perform a series of computer vision tasks including myocardial
segmentation and right ventricular insertion point detection...
