AIMC Topic: Magnetic Resonance Imaging, Cine
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Free-breathing, Highly Accelerated, Single-beat, Multisection Cardiac Cine MRI with Generative Artificial Intelligence.
Radiology. Cardiothoracic imaging
Apr 1, 2025
Purpose To develop and evaluate a free-breathing, highly accelerated, multisection, single-beat cine sequence for cardiac MRI. Materials and Methods This prospective study, conducted from July 2022 to December 2023, included participants with various...
Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.
Radiology. Cardiothoracic imaging
Dec 1, 2024
Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to...
Artificial intelligence-derived stress ejection fraction in stress cardiac magnetic resonance with dipyridamole: bridging past insights with future innovations.
European heart journal. Cardiovascular Imaging
Sep 30, 2024
Artificial intelligence-based fully automated stress left ventricular ejection fraction as a prognostic marker in patients undergoing stress cardiovascular magnetic resonance.
European heart journal. Cardiovascular Imaging
Sep 30, 2024
AIMS: This study aimed to determine in patients undergoing stress cardiovascular magnetic resonance (CMR) whether fully automated stress artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic...
Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank.
European heart journal. Cardiovascular Imaging
Sep 30, 2024
AIMS: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural netwo...
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.
Journal of the American College of Cardiology
Aug 27, 2024
BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).
Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.
Radiology. Cardiothoracic imaging
Jun 1, 2024
Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant ...
Seeing between Time: Higher Frame Rate Cardiac Cine MRI using Deep Learning.
Radiology. Cardiothoracic imaging
Jun 1, 2024
Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.
Radiology. Cardiothoracic imaging
Jun 1, 2024
Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5...
Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance.
European heart journal. Cardiovascular Imaging
May 31, 2024
AIMS: This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular ...
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