Call for paper submission
[new] "Generative Adversarial Networks in Cardiovascular Research", co-editing with Frontiers in Cardiovascular Medicine
Potential DPhil opportunities in "Deep Learning for Cardiovascular MRI", Contact me or Prof. Stefan Piechnik
Qiang Zhang
PhD
British Heart Foundation CRE Intermediate Transition Fellow
Artificial Intelligence in Medicine
I am a deep learning (machine learning) scientist, with expertise in CMR, and cross-domain knowledge of cardiovascular diseases, MR physics and scan protocols. I work on the interpretation and enhancement of gadolinium-free native CMR modalities, particularly quantitative T1-mapping, using novel artificial intelligence approaches. My recent research focus has been on AI Virtual Native Enhancement imaging, where we develop AI techniques that could serve as "virtual contrast dye" to replace intravenous contrast dye. This work has been funded by the John Fell Fund and British Heart Foundation Centre of Research Excellence.
I serve as an Innovation Champion at Oxford University Innovation.
In the press
Oxford University News Features: "How artificial intelligence is shaping medical imaging", 20 Sep 2022
RDM News: "SCMR Early Career Award" -- 8 February 2022
BHF News: "AI breakthrough for faster, cheaper and injection-free heart scans", 9 August 2021
The Telegraph: "New AI heart scanner will cut NHS backlog ...", 7 August 2021
SCMR Newsletter, 29 July 2021
Oxford University Hospitals News: "AI replaces contrast dye for fast, cheaper ...", 8 July 2021
RDM News "AI breakthrough for fast and cheaper CMR scans", 7 July 2021
NIHR Oxford BRC News: "AI replaces contrast dyes for needle-free CMR", 7 July 2021
AHA Circulation: Circulation Podcast, 24 Aug 2021
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BBC Radio 4 Today Interview, on how new AI technologies can help with NHS backlog, 9 August 2021
Times Radio Interview, on AI and robotics in healthcare, 10 August 2021
Key publications
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Artificial Intelligence for Contrast-free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-based Virtual Native Enhancement (VNE).
Journal article
Zhang Q. et al, (2022), Circulation
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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
Recent publications
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Artificial Intelligence for Contrast-free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-based Virtual Native Enhancement (VNE).
Journal article
Zhang Q. et al, (2022), Circulation
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Endogenous T1ρ cardiovascular magnetic resonance in hypertrophic cardiomyopathy.
Journal article
Thompson EW. et al, (2021), J Cardiovasc Magn Reson, 23
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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
Patents
1. 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)
2. 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)
3. 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)