Quantitative assessment of morphological and functional cardiac parameters by
cardiovascular magnetic resonance (CMR) is essential for research and routine clinical …

Abstract Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has
affected nearly 600 million people to date across the world. While COVID-19 is primarily a …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality,
causing over 17. 9 million deaths worldwide per year with associated costs of over $800 …

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse
prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human …

Cardiovascular ageing is a process that begins early in life and leads to a progressive
change in structure and decline in function due to accumulated damage across diverse cell …

Deep learning-based methods for cardiac MR segmentation have achieved state-of-the-art
results. However, these methods can generate incorrect segmentation results which can …

Parametric map** of the heart has become an essential part of many cardiovascular
magnetic resonance imaging exams, and is used for tissue characterization and diagnosis …

Background Cardiac magnetic resonance native T1-map** provides noninvasive,
quantitative, and contrast-free myocardial characterization. However, its predictive value in …

Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR)
image segmentation has achieved state-of-the-art performance. Despite achieving inter …