Gadolinium-free Virtual Native Enhancement for chronic myocardial infarction assessment: independent blinded validation and reproducibility between two centres
Conference paper
THOMPSON P. et al, (2023), Global CMR 2024 Scientific Sessions
Quality control-driven framework for reliable automated segmentation of cardiac magnetic resonance LGE and VNE images
Conference paper
Gonzales RA. et al, (2023)
TVnet: a deep-learning approach for enhanced right ventricular function analysis through tricuspid valve motion tracking
Conference paper
Gonzales RA. et al, (2023)
Myocardial Strain Measurements Derived From MR Feature-Tracking: Influence of Sex, Age, Field Strength, and Vendor.
Journal article
Yang W. et al, (2023), JACC. Cardiovascular imaging
Deep learning for automated insertion point annotation of CMR late gadolinium enhancement and virtual native enhancement images
Conference paper
Gonzales RA. et al, (2023)
Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.
Journal article
Gonzales RA. et al, (2023), Frontiers in cardiovascular medicine, 10
TVnet: automated global analysis of tricuspid valve plane motion in CMR long-axis cines with residual neural networks for assessment of right ventricular function
Conference paper
Gonzales RA. et al, (2022), 36 - 37
Development of Deep Learning Virtual Native Enhancement for Gadolinium-Free Myocardial Infarction and Viability Assessment
Conference paper
ZHANG Q. et al, (2022)
Quality control-driven artificial intelligence for reliable automatic segmentation of LGE images in clinical practice
Conference paper
Gonzales RA. et al, (2022)
1 Long-term prognosis after acute ST-segment elevation myocardial infarction is determined by characteristics in both non-infarcted and infarcted myocardium on cardiovascular magnetic resonance imaging
Conference paper
Shanmuganathan M. et al, (2021), Abstracts
LONG-TERM PROGNOSIS AFTER ACUTE ST-SEGMENT ELEVATION MYOCARDIAL INFARCTION IS DETERMINED BY CHARACTERISTICS IN BOTH NON-INFARCTED AND INFARCTED MYOCARDIUM ON CARDIOVASCULAR MAGNETIC RESONANCE IMAGING
Conference paper
Shanmuganathan M. et al, (2021), HEART, 107, A1 - A1
MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.
Journal article
Gonzales RA. et al, (2021), Frontiers in cardiovascular medicine, 8