Contact information
Colleges
Qiang Zhang
PhD, FSCMR
Associate Professor of AI in Cardiovascular Imaging
- British Heart Foundation Intermediate Fellowship
- Deputy Lead, RDM AI and Medical Big Data CCRT
I am a machine learning scientist working on AI for cardiovascular imaging and population health, based at the Division of Cardiovascular Medicine and Big Data Institute.
My research programme focuses on two complementary areas:
(i) Advancing cardiovascular MRI with deep learning. A representative work is Virtual Native Enhancement (VNE) imaging, where we developed AI techniques that could serve as "virtual contrast" in enhancing MR images, without the need for contrast injections. This technology may lead to more informative, needle-free, faster and safer heart MR scans.
(ii) Studying cardiovascular and cardiometabolic disease through the lens of machine learning and large-scale medical and population data. The goal is to improve cardiovascular population health through AI-enhanced biomarkers and data-driven approaches for risk stratification, prediction and disease prevention.
Key publications
Myocardial Scar Assessment Using Artificial Intelligence-Powered Contrast-Free MRI: A Prospective Multicenter Study of Virtual Native Enhancement.
Journal article
Zhang Q. et al, (2026), Journal of the American College of Cardiology
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
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
Recent publications
Myocardial Scar Assessment Using Artificial Intelligence-Powered Contrast-Free MRI: A Prospective Multicenter Study of Virtual Native Enhancement.
Journal article
Zhang Q. et al, (2026), Journal of the American College of Cardiology
SCAN-MRI: Cardiac MRI-integrated Risk Score for Predicting Cardiovascular Events in Type 2 Diabetes Mellitus.
Journal article
Yang W. et al, (2026), Radiology, 320
Quantifying the Spectrum of Myocardial Fibrosis with Cardiovascular MRI: A Histopathologic Validation Study in Swine.
Journal article
Zhang H. et al, (2026), Radiology. Cardiothoracic imaging, 8
Artificial intelligence in cardiovascular imaging: risks, mitigations and the path to safe implementation.
Journal article
Howard JP. et al, (2026), Heart (British Cardiac Society), 112, 246 - 252
Generalist deep learning for cross-modality landmark annotation in cardiovascular magnetic resonance
Conference paper
Gonzales RA. et al, (2025)