EE Seminar: Breaking the barriers of MRI by exploiting signals structure
(The talk will be given in English)
Speaker: Dr. Lior Weizman
Faculty of EE, Technion and the FMRIB research facility in Oxford University
Monday, January 8th, 2018
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering
Breaking the barriers of MRI by exploiting signals structure
Abstract
Magnetic Resonance Imaging (MRI) is an interdisciplinary field involving physics, engineering, chemistry, mathematics, and neuroscience. It is the best imaging modality for soft tissues and is considered safe as there is no exposure to ionizing radiation. However, it is still highly limited by the physics, in terms of slow scanning time, limited resolution and lack of quantitative information. Recent advances in signal processing theories have enabled breaking those barriers.
I will start the talk by presenting the physical principles of MRI and the conventional solutions to improve MRI by undersampling. I will describe several MRI applications, including structural, functional and quantitative MRI, each involves a unique structure of acquired data. I will show how the methods we developed, that rely on exploiting redundant information within and between MRI scans via sparse-based reconstruction and low-rank modeling, can significantly improve each of those applications. I will show that the theoretical bounds we developed for our methods outperform bounds developed for existing approaches and support the results obtained.
Bio
Lior Weizman is currently a research associate in the Faculty of EE, Technion and the FMRIB research facility in Oxford University. He holds a Ph.D. from the Computer Science and Engineering School at the Hebrew University and M.Sc. and B.Sc. from the EE department at Ben-Gurion University.His research interests include signal processing, magnetic resonance imaging (MRI) and applications of compressed sensing for medical imaging. He previously worked as a signal-processing algorithms developer in various companies in Israel, including RAFAEL, and has served as an advisor for medical imaging and algorithmic solution companies. He is the recipient of the British-Technion Society Fellowship for postdoctoral studies in the UK, the Eshkol and National Foundation for scientific research and engineering Fellowships and the Viterbi Fellowship for postdoctoral studies.
