Date: 20-22 March 2024
Time: 9:00 AM ET (New York Time)
Presenter(s): Dr. Philippe Ciuciu
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This course aims to provide a self-contained view of modern data acquisition and image reconstruction aspects in magnetic resonance imaging (MRI) from an engineering perspective with still physics based knowledge. To this end, it will specifically cover both model based and data driven computational approaches in MRI, concerning both accelerated data acquisition and image reconstruction strategies. It is specifically tailored to graduate students, researchers and industry professionals working in the medical imaging field who want to know more about the radical shift machine learning (ML) has introduced for MRI during the last few years.
As MRI is the most widely used medical imaging technique for non-invasively probing soft tissues in the human body (brain, heart, breast, liver, etc), training PhD students, postdocs and researchers in electrical and biomedical engineering is strategic for cross-fertilizing the fields and for understanding the ML-related needs and expectations from the MRI side.
In the last decade, the application of Compressed Sensing (CS) theory to MRI has received considerable interest and led to major improvements in terms of accelerating data acquisition without degrading image quality in low acceleration regimes. Two recent complementary research directions are starting to supplant this classical CS setting to reach highly accelerated regimes: First, the advent of deep learning solutions for MR image reconstruction, and second, the design of optimization and learning-based under-sampling schemes, notably non-Cartesian trajectories. Taken together, the combination of these approaches in a joint learning based framework offers new perspectives for valuable clinical applications.
Dr. Philippe Ciuciu obtained his PhD in electrical engineering in 2000 and his Habilitation to Conduct Research degree in 2008, both from the University of Paris-Sud (Orsay, France). His career began at the CEA (The French Alternative Energies and Atomic Energy Commission). In 2012-2013 he was invited by the Department of Applied Mathematics at the University of Toulouse as a guest professor.Dr. Ciuciu is now a CEA Fellow and Research Director at NeuroSpin (CEA Paris-Saclay), the largest ultra-high field MRI center dedicated to cognitive and clinical neuroscience research. He has a joint appointment with Inria, the French Institute for Research in Digital Science and Technology where he has led, since 2018, the Compressed Sensing team in the former Inria-CEA Parietal team. Since April 2022, due to his expertise in neuroimaging techniques, brain data analysis, and machine learning, he has been leading the new joint Inria-CEA MIND (Models and Inference for Neuroimaging Data) lab, a cutting edge research unit of 40 researchers and staff.
His inter-disciplinary research interests range from methodological developments in accelerated MRI to cutting-edge signal processing tools for the analysis of functional brain data (fMRI, M/EEG) with various applications in neuroscience. He has over 200 peer-reviewed publications, 6 MRI related patents and more than 65 articles in prominent international journals.Since 2019, he has held the position as Senior Area Editor for the IEEE open Journal of Signal Processing and from 2020, he was appointed as Associate Editor to Frontiers in Neuroscience, section: Brain imaging methods. In 2022, he joined the editorial board of IEEE Transactions on Medical Imaging. From 2019-2021, Dr. Ciuciu was Vice Chair for the Biomedical Image and Signal Analytics (BISA) technical committee of the EURASIP society. Additionally, he was part of the conference program committee at the 2021 ESMRMB conference, a role that he renewed for the next edition in 2023. Concomitantly, he was a member of the steering committee of the International Symposium on Biomedical Imaging for 2020-2022 where he held the role of the representative of the IEEE Signal Processing society.