Qiang Zhang

PhD, MEng, BEng, BSc

Postdoctoral Research Fellow

Deep Learning in Cardiovascular Magnetic Resonance (CMR)

I am a deep learning researcher, with expertise in clinical CMR image analysis, and domain knowledge in CMR physics and cardiovascular diseases. My research focus is on the interpretation of gadolinium-free native CMR modalities, in particular quantitative T1-mapping, using novel AI approaches.

Key publications

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.
Journal article

Zhang Q. et al, (2020), Artif Intell Med, 110
Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
Conference paper

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
Active appearance pyramids for object parametrisation and fitting
Journal article

Zhang Q. et al, (2016), Medical Image Analysis, 32, 101 - 114

Recent publications

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.
Journal article

Zhang Q. et al, (2020), Artif Intell Med, 110
Total Mapping Toolbox (TOMATO): An open source library for cardiac magnetic resonance parametric mapping
Journal article

Werys K. et al, (2020), SoftwareX, 11
Pitfalls of blinded T1-map image analysis by human operator and impact on measured stress T1 responses
Conference paper

Masi A. et al, (2019), EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, 20, 129 - 130
Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
Conference paper

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
Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification
Journal article

Zhang Q. et al, (2017), International Journal of Computer Assisted Radiology and Surgery, 12, 1271 - 1280

More publications