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Powering Next-Generation MRI with Physics, Information, and Computation: An Integrated Encoding and Decoding Approach
Friday, October 16, 2020
9:30AM – 10:30AM
Zoom Meeting

Bo Zhao

Magnetic resonance imaging (MRI) is a powerful and versatile imaging technology, which has provided unprecedented capabilities to probe the structural, functional, and metabolic information of living systems. Since its inception, the MR imaging process has been formulated as a “communication” problem – i.e., it involves both encoding and decoding. The encoding process maps an underlying image function that depends on physical, physiological, and experimental parameters into sensory data utilizing spin physics; and the decoding process reconstructs this desired image function from the measured data. This long-standing encoding/decoding model often results in poor trade-offs between image resolution, signal-to-noise ratio, and data acquisition speed, which limits the practical utility of high-dimensional MRI.

In this talk, I will present a novel imaging framework to tackle these challenges, by using an integrated encoding and decoding paradigm. The proposed framework leverages advanced mathematical models and algorithms to tightly integrate the encoding and decoding processes. It exploits the synergistic interactions between spin physics, statistical inference, and machine learning to help overcome major technical barriers of the existing MRI techniques. I will illustrate the power of this framework using two concrete approaches, i.e., subspace imaging and statistical imaging, and will highlight their impacts on applications in cardiovascular imaging and quantitative neuroimaging. Finally, I will discuss some exciting new opportunities with this framework that leverage the rapid development of advanced computing and machine learning technologies.

Bo Zhao is an Assistant Professor at the Oden Institute for Computational Engineering and Sciences and the Department of Biomedical Engineering. His research is in the general area of computational imaging and medical imaging, which lies at the intersection of imaging science and data science. Specifically, his group focuses on developing novel mathematical models, computational algorithms, and data acquisition schemes to address inverse problems in magnetic resonance (MR) imaging. His group takes unique approaches that synergistically integrate spin physics, information processing, and advanced computing to push the performance limits of MR imaging systems.

(The Babuška Forum series was started by Professor Ivo Babuška several years ago to expose students to interesting and curious topics relevant to computational engineering and science with technical content at the graduate student level (i.e. the focus of the lectures is on main ideas with some technical content). Seminar credit is given to those students who attend.)

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Hosted by Stefan Henneking


 Event Stream Link: Click Here to Watch