Dr. Chen Qin

Machine Learning in Magnetic Resonance Image Reconstruction and Analysis

Dr. Chen Qin



Machine Learning has shown great potential in improving the entire medical imaging workflow, from image acquisition and reconstruction, to disease diagnosis and treatment. This talk will mainly discuss about the recent advances of deep learning in medical imaging, and particularly focus on our recent achievements in magenetic resonance image (MRI) reconstruction and analysis. Firstly, I will introduce the model-based deep learning research on acceleratred dynamic MRI reconstruction, and discuss about its application to dynamic cardiac MRI cine imaging. Then, I will focus on medical image analysis methods with limited supervision, including the weakly-supervised image segmentation, self-/unsupervised mono-/multi-modal image registration, and biomechanics-informed learning for myocardial motion tracking. Lastly, I will briefly talk about the integration of MRI reconstruction and analysis, which can potentially facilitate the fast and personalized MRI imaging and analysis.



Dr. Chen Qin is a Lecturer (eq. Assistant Professor) in Computer Vision and Machine Learning at Electronics and Electrical Engineering, The University of Edinburgh, UK. She is also an Honorary Research Fellow at Imperial College London. Before that, she worked as a Research Associate at Department of Computing, Imperial College London. She obtained her Ph.D. in Computing Research from Imperial College London in January 2020, M.Sc. from Tsinghua University, and B.Eng. from Harbin Institute of Technology. From July 2018 to May 2019, she also worked at Siemens Healthineers (NJ, USA) and Huawei Technology (London, UK) as a research intern.


Her research is at the interdisciplinary field of artificial intelligence and medical imaging, aiming to improve the entire medical imaging/radiology workflow with significant impact for clinical use via machine intelligence. Her current research mainly focuses on the development of machine learning algorithms for magnetic resonance image reconstruction and analysis, including dynamic MR image reconstruction, medical image registration and segmentation. She has won the runner-up for 2019 Facebook AI Research/NYU fastMRI challenge, and has also achieved ISMRM/MICCAI/CMR Travel Award. She has published over 30 papers on top-tier engineering and medical imaging journals and conference proceedings including IEEE TMI, NeuroImage Clinical, MRM, MICCAI, IPMI, ISMRM, AAAI etc., with over 800 google scholar citations. She also serves as PC members and reviewers for several top conferences and journals. Personal Website:

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