Abnormalities in heart wall motion are often related to cardiovascular disease (CVD), the
first cause of death worldwide. In this context, tag-MRI imaging technique has the
potential to become the gold-standard for quantifying regional function and therefore to
enable reliable stratification of CVDs patients. However, there is a lack of comprehensive
image analysis methods that can analyse the cardiac motion from tag-MRI. On the other
hand, the radiomics paradigm has recently shown great promise ...
Abnormalities in heart wall motion are often related to cardiovascular disease (CVD), the
first cause of death worldwide. In this context, tag-MRI imaging technique has the
potential to become the gold-standard for quantifying regional function and therefore to
enable reliable stratification of CVDs patients. However, there is a lack of comprehensive
image analysis methods that can analyse the cardiac motion from tag-MRI. On the other
hand, the radiomics paradigm has recently shown great promise for patient stratification
in the presence of complex diseases in cardiac MRI.
In this project, the feasibility of using radiomics and machine learning for cardiac
stratification in tag-MRI is investigated through a number of methods and experiments.
First, a learning radiomics-based approach is implemented to predict the motion
landmarks conventionally defined through semi-automatic methods, indicating limited
correlations between the two types of variables. Subsequently, the potential of radiomics
for cardiac motion stratification in tag-MRI is implemented based on feature selection
and principal component analysis. The results obtained based on a public database of 15
tag-MRI cases show that, unlike the motion landmarks used in previous research,
radiomics features estimated from tag-MRI have the potential for discriminating between
distinct cardiovascular subgroups.
This thesis represents the first proof-of-concept study for deeper phenotyping and
advanced stratification of cardiac motion using tag-MRI. Future work includes more
extensive validation with larger clinical samples and diverse CVD subgroups
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