Abstract Machine learning (ML) has been increasingly used within cardiology, particularly in
the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML …

The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the
visualization and interpretation of altered morphological structures and function of the heart …

We propose to learn a low-dimensional probabilistic deformation model from data which can
be used for the registration and the analysis of deformations. The latent variable model …

Background: Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is
suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left …

Atlases encode valuable anatomical and functional information from a population. In this
work, a bi-ventricular cardiac atlas was built from a unique data set, which consists of high …

Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data
provides an intuitive understanding of the concepts of basic statistics, with a focus on solving …

Heart disease is the leading cause of death in the modern world. Cardiac imaging is
routinely applied for assessment and diagnosis of cardiac diseases. Computerized image …

Computational models of the heart are increasingly being used in the development of
devices, patient diagnosis and therapy guidance. While software techniques have been …

The loss of cardiac pump function accounts for a significant increase in both mortality and
morbidity in Western society, where there is currently a one in four lifetime risk, and costs …

We propose an independent objective method to characterize different patterns of functional
responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome …