Websites
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OXFORD BHF CRE Profile Webpage
Oxford British Heart Foundation Centre of Research Excellence
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AI breakthrough for fast an cheaper CMR scans
Oxford RDM news release
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AI replaces contrast dyes for needle-free CMR
NIHR Oxford BRC news release
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 install and validate the methods correctly before using them for clinical diagnosis or multi-centre trials. We have developed a robust and practicable phantom quality assurance approach to assure the acute installation and standardisation 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
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Towards 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
Recent publications
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Towards 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
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Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods.
Journal article
Burrage MK. et al, (2021), Sci Rep, 11
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Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.
Journal article
Hann E. et al, (2021), Med Image Anal, 71
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Cardiovascular magnetic resonance stress and rest T1-mapping using regadenoson for detection of ischemic heart disease compared to healthy controls.
Journal article
Burrage MK. et al, (2021), Int J Cardiol
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Standardization of T1-mapping in cardiovascular magnetic resonance using clustered structuring for benchmarking normal ranges.
Journal article
Popescu IA. et al, (2021), Int J Cardiol, 326, 220 - 225
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Quality assurance of quantitative cardiac T1-mapping in multicenter clinical trials - A T1 phantom program from the hypertrophic cardiomyopathy registry (HCMR) study.
Journal article
Zhang Q. et al, (2021), Int J Cardiol
Patents
1. 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)
2. 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)