Deep Learning for Fast MR Imaging and Analysis
Deep learning has shown great potential in improving and accelerating the entire medical imaging workflow, from image acquisition to interpretation. This talk will focus on the recent advances of deep learning in medical imaging, from the reconstruction of accelerated signals to automatic quantification of clinically useful information. The talk will describe how model-based deep learning can be used for reconstruction of accelerated MRI and will discuss its applications to fast dynamic cardiac MRI cine imaging. It will also show the utility of deep learning for fast analysis of medical images, with a particular focus on image registration and motion tracking. Finally, it will briefly discuss about the open challenges and opportunities of AI in medical imaging.
Speaker Bio – Dr Chen Qin
Dr. Chen Qin is a Lecturer in Computer Vision and Machine Learning at Department of Electrical and Electronic Engineering and I-X, Imperial College London. Previously, she was a Lecturer at School of Engineering, University of Edinburgh. She obtained her Ph.D. in Computing Research from Imperial College London in January 2020. Her research is interdisciplinary in nature and at the intersection between machine learning and medical imaging. Her current research mainly focuses on the development of effective and robust machine learning algorithms for medical image computing and analysis, with a vision towards improving medical imaging workflow via machine intelligence for significant impact in clinical use. She is currently an EPSRC New Investigator Award and EPSRC Early Career Researcher International Collaboration Grant holder. She also served as an area chair for MICCAI 2022/23 and a member of organising and programme committee at several international workshops and challenges.
Time: 12.30 – 13.30
Date: Tuesday 14 November
Location: I-X 5 Meeting Room, Level 5
Translation and Innovation Hub (I-HUB)
Imperial White City Campus
84 Wood Lane
Any questions, please contact Eileen Boyce (firstname.lastname@example.org) or Lauren Burton (email@example.com).