Qiang Zhang
PhD, FSCMR
Associate Professor of AI in Cardiovascular Imaging
- British Heart Foundation Intermediate Fellowship
- British Heart Foundation CRE Transition Fellowship
I am a deep learning (machine learning) scientist, with cross-domain knowledge of cardiovascular disease, MR physics and scan protocols. My research programme focuses on (i) advancing cardiac imaging and (ii) enriching clinical research, with AI:
(i) Advancing cardiovascular MRI with Generative AI. 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 may lead to more informative, needle-free, faster and safer heart MR scans.
(ii) Developing automated image processing pipeline for cardiac imaging big data, and AI-enhanced cardiac biomarkers to drive new discoveries in clinical research.
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), J Am Coll Cardiol
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), Radiol Cardiothorac Imaging, 8
Artificial intelligence in cardiovascular imaging: risks, mitigations and the path to safe implementation.
Journal article
Howard JP. et al, (2026), Heart, 112, 246 - 252
Generalist deep learning for cross-modality landmark annotation in cardiovascular magnetic resonance
Conference paper
Gonzales RA. et al, (2025)