Objectives The aim of this study was to determine whether left ventricular ejection fraction
(LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) …

Background Theoretically, artificial intelligence can provide an accurate automatic solution
to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic …

Purpose To develop a deep learning–based method for fully automated quantification of left
ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a …

Cardiovascular magnetic resonance (CMR) imaging provides reliable assessments of
biventricular morphology and function. Since manual post-processing is time-consuming …

Objectives This study sought to develop a fully automated framework for cardiac function
analysis from cardiac magnetic resonance (CMR), including comprehensive quality control …

To investigate the performance of a deep learning-based algorithm for fully automated
quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively …

Background Conventional segmented, retrospectively gated cine (Conv‐cine) is challenged
in patients with breath‐hold difficulties. Compressed sensing (CS) has shown values in cine …

Cardiac magnetic resonance imaging–derived biventricular mass and function parameters,
such as end-systolic volume, end-diastolic volume, ejection fraction, stroke volume (SV), and …

Objectives: To evaluate the performance of a deep learning-based method to automatically
quantify left ventricular (LV) function from MR images in different cardiomyopathy. Methods …

Background Right ventricular (RV) function is important in the evaluation of cardiac function,
but its assessment using standard transthoracic echocardiography (TTE) remains …