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Pericardium

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Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi-supervised method with difference-guided consistency.

Medical physics
BACKGROUND: Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis and treatment of cardiovascular diseases. Precise segmentation is challenging due to high co...

Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep le...

Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.

BMC medical imaging
BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase ...

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.

JACC. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been ...

Epicardial adipose tissue and myocardial ischemia assessed by computed tomography perfusion imaging and invasive fractional flow reserve.

Journal of cardiovascular computed tomography
BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active fat depot that is associated with incident coronary artery disease (CAD) and major adverse cardiovascular events. The relationship between EAT and myocardial ischemia remains uncle...

Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans.

Scientific reports
Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we develope...

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.

Scientific reports
To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT ima...