LIANG ZHONG

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Assoc Prof

Duke-NUS

Singapore

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About

Liang Zhong is an Associate Professor at Duke-NUS, Singapore. His research areas focus on cardiovascular magnetic resonance imaging, including studies on left ventricular flow kinetic energy, mitral regurgitation quantification, and the application of artificial intelligence in predicting cardiovascular outcomes. He has contributed significantly to the understanding of right ventricular biomarkers and their association with exertional capacity in pulmonary hypertension, as well as advancements in 4D flow CMR technology.

Recent Grants

Grant: Close

Conglomerating the effects of physical activity on cardiovascular longevity in ageing

Open Date: 2021-11-01

Close Date: 2023-10-01

Grant: Close

Evaluating dynamic myocardial blood flow quantitative as a cost effective care model for diabetic patients with coronary artery disease: A randomized controlled trial

Open Date: 2021-10-01

Close Date: 2024-10-01

Grant: Close

AI driven national Platform for CT cOronary angiography for clinicaL and industriaL applicatiOns (APOLLO)

Open Date: 2021-06-01

Close Date: 2024-06-01

Grant: Close

An Artificial Intelligence Enabled Wearable TCM Pulse Analyser for Heart Failure Detection

Open Date: 2021-03-01

Close Date: 2023-03-01

Grant: Close

Effectiveness and cost-effectiveness of noninvasive fractional flow reserve for physiological assessment in suspected coronary artery disease (ECONOMY)

Open Date: 2020-02-01

Close Date: 2024-02-01

Articles (11)

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2024

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (7)

Michele D’Alto

ITALY

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Jim Wild

Professor of Magnetic Resonance Physics

University of Sheffield

UNITED KINGDOM

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Hosamadin S Assadi

University of East Anglia

UNITED KINGDOM

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Henning Gall

Prof Dr Dr

GERMANY

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Rob van der Geest

Associate Professor

Leiden University Medical Center

NETHERLANDS

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George Thornton

University College London

UNITED KINGDOM

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Pankaj Garg

UNITED KINGDOM

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LIANG ZHONG

Research Interests

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About

Recent Grants

Articles (11)

Editorial for “Evaluation of Left Ventricular Flow Kinetic Energy by Four‐Dimensional Blood Flow MRI in Non‐Dialysis Chronic Kidney Disease Patients”

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions

Effect of psychosocial motivations and technology on physical activity behaviours among community older men and women

Right ventricular energetic biomarkers from 4D Flow CMR are associated with exertional capacity in pulmonary arterial hypertension

Imaging the right atrium in pulmonary hypertension: a systematic review and meta-analysis

Validation of aortic valve pressure gradient quantification using semi-automated 4D flow CMR pipeline

Ventricular flow analysis and its association with exertional capacity in repaired tetralogy of Fallot: 4D flow cardiovascular magnetic resonance study

Kat-ARC accelerated 4D flow CMR: clinical validation for transvalvular flow and peak velocity assessment

Editorial for “Left Ventricular Strain Measurements Derived from MR Feature Tracking: A Head‐to‐Head Comparison of a Higher Temporal Resolution Method with a Conventional Method”

The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review

Editorial for “Quiescent‐Interval Slice‐Selective (QISS) MRI Accurately Estimates Intravascular Stent Dimensions Prior to Intervention in Patients with Peripheral Artery Disease”

Collaborators (7)

Michele D’Alto

Jim Wild

Hosamadin S Assadi

Henning Gall

Rob van der Geest

George Thornton

Pankaj Garg

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