NIHR Oxford BRC Postdoctoral Research Fellow
- Imaging Scientist
- DPhil in cardiac wall motion abnormality assessment
- Deep learning, AI, parametric T1 mapping
Postdoctoral Research Fellow @RDMOxford @OxfordBRC focused on biomedical image analysis, with application to cardiac T1 mapping standardization across multiple centres world-wide. I have particular interest in methods for data visualisation and automated quality control (QC). Through my PhD in digital health at the University of Oxford I have developed extensive experience in medical imaging and data sciences.
I am passionate about translating research to clinical practice and patient care. Being directly involved with clinical trials and large-scale multi-centre studies, I have extensive experience in working in highly diverse, multidisciplinary teams including at executive level, and work at the interface of industry and academia.
Funding: My current research is funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre.
Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods.
Burrage MK. et al, (2021), Sci Rep, 11
Quality assurance of quantitative cardiac T1-mapping in multicenter clinical trials - A T1 phantom program from the hypertrophic cardiomyopathy registry (HCMR) study.
Zhang Q. et al, (2021), Int J Cardiol, 330, 251 - 258
Standardization of T1-mapping in cardiovascular magnetic resonance using clustered structuring for benchmarking normal ranges.
Popescu IA. et al, (2021), Int J Cardiol, 326, 220 - 225
Total Mapping Toolbox (TOMATO): An open source library for cardiac magnetic resonance parametric mapping
Werys K. et al, (2020), SoftwareX, 11
Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
Hann E. et al, (2019), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11765 LNCS, 750 - 758
Myocardial scar quantification using SLIC supervoxels - parcellation based on tissue characteristic strains
Popescu IA. et al, (2017), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10124 LNCS, 182 - 190
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, Werys K, Popescu IA, Ferreira VM: “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)