  Valve annuli motion and morphology, measured from non-invasive imaging, can
be used to gain a better understanding of healthy and pathological heart
function. Measurements such as long-axis strain as well as peak strain rates
provide markers of systolic function. Likewise, early and late-diastolic
filling velocities are used as indicators of diastolic function. Quantifying
global strains, however, requires a fast and precise method of tracking
long-axis motion throughout the cardiac cycle. Valve landmarks such as the
insertion of leaflets into the myocardial wall provide features that can be
tracked to measure global long-axis motion. Feature tracking methods require
initialisation, which can be time-consuming in studies with large cohorts.
Therefore, this study developed and trained a neural network to identify ten
features from unlabeled long-axis MR images: six mitral valve points from three
long-axis views, two aortic valve points and two tricuspid valve points. This
study used manual annotations of valve landmarks in standard 2-, 3- and
4-chamber long-axis images collected in clinical scans to train the network.
The accuracy in the identification of these ten features, in pixel distance,
was compared with the accuracy of two commonly used feature tracking methods as
well as the inter-observer variability of manual annotations. Clinical
measures, such as valve landmark strain and motion between end-diastole and
end-systole, are also presented to illustrate the utility and robustness of the
method.
