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Qiang Zhang

PhD, MEng, BEng, BSc

British Heart Foundation CRE Intermediate Research Fellow

Deep Learning in Cardiovascular Magnetic Resonance

I have a background in deep learning (machine learning), with expertise in clinical CMR image analysis, and domain knowledge in CMR physics and cardiovascular diseases. My research focus is on the interpretation and enhancement of gadolinium-free native CMR modalities, in particular quantitative T1-mapping, using novel artificial intelligence approaches. My current research 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 the Oxford University Innovation.

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