Exploring cardiovascular involvement in IgG4-related disease: a case series approach with cardiovascular magnetic resonance.
Journal article
Henry JA. et al, (2024), Heart
Generative AI Virtual Contrast for Cardiovascular Magnetic Resonance: A Pathway to Needle-Free and Fast Imaging of Myocardial Infarction?
Journal article
Fok WYR. and Zhang Q., (2024), Circ Cardiovasc Imaging
Improving the efficiency and accuracy of CMR with AI - review of evidence and proposition of a roadmap to clinical translation.
Journal article
Zhang Q. et al, (2024), J Cardiovasc Magn Reson
Deep learning for automated insertion point annotation of CMR T1 maps
Conference paper
Gonzales RA. et al, (2024)
Automated CMR Index of Left Ventricular Diastolic Function Post-acute Myocardial Infarction Provides Independent and Incremental Prediction of Long-term Prognosis When Added to Conventional Indices
Conference paper
Shanmuganathan M. et al, (2024), Journal of Cardiovascular Magnetic Resonance, 26, 100268 - 100268
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 Cardiovasc 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)
Acute Response in the Noninfarcted Myocardium Predicts Long-Term Major Adverse Cardiac Events After STEMI.
Journal article
Shanmuganathan M. et al, (2023), JACC Cardiovasc Imaging, 16, 46 - 59
Editorial: Generative adversarial networks in cardiovascular research.
Journal article
Zhang Q. et al, (2023), Front Cardiovasc Med, 10
Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.
Journal article
Gonzales RA. et al, (2023), Front Cardiovasc Med, 10
Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.
Journal article
Zhang Q. et al, (2022), Circulation, 146, 1492 - 1503
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), European Heart Journal - Cardiovascular Imaging, 23
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, A1.1 - A1
Endogenous T1ρ cardiovascular magnetic resonance in hypertrophic cardiomyopathy.
Journal article
Thompson EW. et al, (2021), J Cardiovasc Magn Reson, 23
Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy.
Journal article
Zhang Q. et al, (2021), Circulation, 144, 589 - 599