Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

News Coverage

"AI breakthrough for fast and cheaper CMR scans"   -- Oxford RDM news page, 7 July 2021

"AI replaces contrast dyes for needle-free CMR"   -- NIHR Oxford BRC news page, 7 July 2021

"AI replaces contrast dye for fast, cheaper ..."  -- Oxford University Hospitals news, 8 July 2021

"New AI heart scanner will cut NHS backlog ..." -- The Telegraph, 7 August 2021

SCMR Newsletter, 29 July 2021

"AI breakthrough for faster, cheaper and injection-free heart scans" -- BHF news page

Circulation Podcast

Public engagement

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

Social media

Qiang Zhang

PhD, MEng, BEng, BSc


British Heart Foundation CRE Intermediate Transition Fellow

Deep Learning in Cardiovascular Magnetic Resonance

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 also work actively on CMR T1-mapping standardisation. T1-mapping offers a unique opportunity for myocardial tissue characterization. However, for clinical sites attempting to implement T1 mapping, it is often unclear how to validate the methods correctly before using them for clinical diagnosis. We have developed a robust and practicable phantom quality assurance approach to assure the accurate installation of CMR T1-mapping methods. The programme aims to translate T1-mapping into widespread use in a real-life setting.

I serve as an Innovation Champion at Oxford University Innovation.

Key publications

Recent publications

More publications

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)