Potential DPhil opportunities in "Deep Generative Models for Cardiovascular MRI"
Qiang Zhang
PhD, FSCMR
British Heart Foundation CRE 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, using novel artificial intelligence approaches. My recent research focus has been on AI Virtual Native Enhancement (VNE) imaging, where we develop AI techniques that could serve as "virtual contrast dye" to replace intravenous contrast dye. My work has been funded by the John Fell Fund and Oxford British Heart Foundation Centre of Research Excellence.
[New] VNE Open Research Platform: https://gitlab.com/ai-virtual-contrast/VNE
In the press
News articles
- The Sunday Times: "AI slashes cost of MRI scans ...", 01 Jan 2023
- Oxford University News: "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
- OUH 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
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 August 2021
- Times Radio Interview, on AI and robotics in healthcare, 10 August 2021
Recent publications
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Gadolinium-free Virtual Native Enhancement for chronic myocardial infarction assessment: independent blinded validation and reproducibility between two centres
Conference paper
THOMPSON P. et al, (2023), Global CMR 2024 Scientific Sessions
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Quality control-driven framework for reliable automated segmentation of cardiac magnetic resonance LGE and VNE images
Conference paper
Gonzales RA. et al, (2023)
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TVnet: a deep-learning approach for enhanced right ventricular function analysis through tricuspid valve motion tracking
Conference paper
Gonzales RA. et al, (2023)
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)