Contact information
Colleges
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.
In the press
News articles
• The Sunday Times: "AI slashes cost of MRI scans ...", 1 Jan 2023
• Oxford University News: "How AI is shaping medical imaging", 20 Sep 2022
• RDM News: "SCMR Early Career Award" 8 Feb 2022
• BHF News: "AI breakthrough for faster, cheaper and injection-free heart scans", 9 Aug 2021
• The Telegraph: "New AI heart scanner will cut NHS backlog ...", 7 Aug 2021
• SCMR Newsletter, 29 Jul 2021
• OUH News: "AI replaces contrast dye for fast, cheaper ...", 8 Jul 2021
• RDM News "AI breakthrough for fast and cheaper CMR scans", 7 Jul 2021
• NIHR Oxford BRC News: "AI replaces contrast dyes for needle-free CMR", 7 Jul 2021
Editorial
Circulation Podcast, 24 Aug 2021
Circulation Podcast, 15 Nov 2022
Circulation Editorial, 2021
Circulation Editorial, 2022
Interview
BBC Radio 4 Today Interview, on how new AI technologies can help with NHS backlog, 9 Aug 2021
Times Radio Interview, on AI and robotics in healthcare, 10 Aug 2021
Key publications
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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
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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
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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
Recent publications
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Right Ventricular Strain Improves Cardiac MRI-based Prognostication in Heart Failure with Preserved Ejection Fraction.
Journal article
Zhu L. et al, (2025), Radiology, 315
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Prognosis and Risk Stratification in Dilated Cardiomyopathy With LVEF≤35%: Cardiac MRI Insights for Better Outcomes.
Journal article
Zhou D. et al, (2025), Circ Cardiovasc Imaging, 18
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Exploring cardiovascular involvement in IgG4-related disease: a case series approach with cardiovascular magnetic resonance.
Journal article
Henry JA. et al, (2025), Heart, 111, 133 - 140
Patents
Zhang Q, Piechnik SK, Ferreira VM, Hann E, Popescu IA: “Enhancement of Medical Images”, Oxford University Innovation, PCT/GB2020/052117, published 11 March 2021 (Publication number WO/2021/044153)
Zhang Q, Piechnik SK, Ferreira VM, Werys K, Popescu IA: “Validation of Quantitative Magnetic Resonance Imaging Protocols”, Oxford University Innovation, PCT/GB2020/051189, published 26 Nov 2020 (Publication number WO/2020/234570)
Hann E, Piechnik SK, Popescu IA, Zhang Q, Werys K, Ferreira VM: “Method and Apparatus for Quality Prediction”, Oxford University Innovation, PCT/GB2020/050249, published 13 Aug 2020 (Publication number WO/2020/161481)