Medical Engineering Special Seminar, Ed Wu
Despite a half-century of advancements, global MRI accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using permanent 0.05 Tesla magnets and deep learning for electromagnetic interference elimination, we developed low-cost and RF shielding-free MRI scanners that operate using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its applicability for imaging brain (Nature Communications 2021) and various anatomical structures at whole-body level (Science 2024). Furthermore, we developed three-dimensional deep learning reconstruction methodologies to boost image quality by harnessing extensive high-field MRI data (MRM 2023, Science Advances 2023, Science 2024). We envision that these ongoing advances will eventually lead to an entirely new class of affordable and computing–powered ultra-low-field MRI scanners for point-of-care healthcare applications, and neuroscience utility such as brain health monitoring.
Biography: Dr. Wu is the Lam Woo Professor and Chair of Biomedical Engineering at the University of Hong Kong. He obtained his BEng in Electrical Engineering from Tianjin University in 1984, MSc in Medical Physics from University of Wisconsin – Madison in 1988, and PhD in Radiological Sciences from University of California – Irvine in 1993. From 1990 to 2003, Dr. Wu worked in Columbia University first as an Assistant Professor and later as an Associate Professor of Radiology and Biomedical Engineering. Dr. Wu moved to University of Hong Kong in 2003. At present, his two research focuses are to re-engineer MRI systems for healthcare through computing and imaging at ultra-low-field, and to develop fMRI methodologies for interrogating brain long-range circuits by combing fMRI, electrophysiology, optogenetics and computational modeling. Dr. Wu is an elected Fellow of ISMRM, IEEE, AIMBE, and Hong Kong Academy of Engineering.
http://www.eee.hku.hk/people/ewu ; http://www.hku.hk/bisplab/