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Publications

Prognosis and Risk Stratification in Dilated Cardiomyopathy With LVEF≤35%: Cardiac MRI Insights for Better Outcomes.

Zhou D. et al, (2025), Circ Cardiovasc Imaging, 18

Exploring cardiovascular involvement in IgG4-related disease: a case series approach with cardiovascular magnetic resonance.

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?

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.

Zhang Q. et al, (2024), J Cardiovasc Magn Reson

Deep learning for automated insertion point annotation of CMR T1 maps

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

Shanmuganathan M. et al, (2024)

Gadolinium-free Virtual Native Enhancement for chronic myocardial infarction assessment: independent blinded validation and reproducibility between two centres

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

Gonzales RA. et al, (2023)

TVnet: a deep-learning approach for enhanced right ventricular function analysis through tricuspid valve motion tracking

Gonzales RA. et al, (2023)

Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images

Gonzales RA. et al, (2023), Frontiers in Cardiovascular Medicine

Myocardial Strain Measurements Derived From MR Feature-Tracking: Influence of Sex, Age, Field Strength, and Vendor.

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

Gonzales RA. et al, (2023)

Acute Response in the Noninfarcted Myocardium Predicts Long-Term Major Adverse Cardiac Events After STEMI.

Shanmuganathan M. et al, (2023), JACC Cardiovasc Imaging, 16, 46 - 59

Editorial: Generative adversarial networks in cardiovascular research.

Zhang Q. 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.

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

Gonzales RA. et al, (2022), 36 - 37

Development of Deep Learning Virtual Native Enhancement for Gadolinium-Free Myocardial Infarction and Viability Assessment

ZHANG Q. et al, (2022)

Quality control-driven artificial intelligence for reliable automatic segmentation of LGE images in clinical practice

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

Shanmuganathan M. et al, (2021), Abstracts

Endogenous T1ρ cardiovascular magnetic resonance in hypertrophic cardiomyopathy.

Thompson EW. et al, (2021), J Cardiovasc Magn Reson, 23

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