Haikun Qi

Release time:2021-10-14Viewed:378

Brief CV:

Dr. Haikun Qi is an assistant professor of School of Biomedical Engineering and is the PI of cardiovascular MRI Lab. Dr. Qi received her PhD degree in Department of Biomedical Engineering at Tsinghua University in 2018. After her PhD, she worked as a postdoctoral fellow at King's College London, UK.

Dr. Qi’s research interests lie in developing fast and/or quantitative MRI techniques for the evaluation of cardiovascular diseases. She is currently working on developing next-generation cardiovascular MRI techniques, including 3D high-resolution single-sequence multi-modality cardiac MRI and exploration of deep learning to enhance the performance of data acquisition, motion correction, image reconstruction and disease diagnosis. Dr. Qi has published several peer-reviewed papers as the first author or corresponding author in top journals of the field of MRI and medical image processing, including Radiology, Journal of Cardiovascular Magnetic Resonance, Magnetic Resonance in Medicine, IEEE Transactions on Medical Imaging, et al.

Research Interests:

  • Cardiac MRI; MRI of atherosclerosis and intracranial aneurysms

  • Fast and/or quantitative MRI; undersampled MRI reconstruction

  • Deep learning in cardiovascular MRI


Principles of Magnetic Resonance Imaging

Services to External Academic Communities:

Reviewing activities:

Review Editor for Frontiers in Cardiovascular Medicine

Reviewer for Magnetic Resonance in Medicine, Journal of Cardiovascular Magnetic Resonance, IEEE Trans Med Imaging, BMC Medical Imaging, MICCAI, ISMRM, SMRA


International Society of Magnetic Resonance in Medicine (ISMRM)

Medical Image Computing and Computer Assisted Intervention (MICCAI)

Society for Magnetic Resonance Angiography (SMRA)


Lab Introduction:

The Cardiovascular MRI Lab is devoted to:

  • Development of efficient, multimodal cardiac MRI techniques: design of fast imaging sequences, motion correction, image reconstruction, and quantitative parametric mapping;

  • Cardiac image analysis: develop methods for multi-modal or multi-parametric cardiac image analysis for screening, grading and prediction of cardiac diseases;

  • Application of deep learning in cardiovascular MRI: develop deep learning techniques to optimize the entire workflow of cardiovascular MRI, including data acquisition, image reconstruction, image processing and disease diagnosis; design a new generation of cardiovascular MRI techniques based on deep learning.