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Potential DPhil opportunities in "Deep Generative Models for Cardiovascular MRI"

Qiang Zhang


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

In the press

News articles



  • 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


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)