SUNMONTUEWEDTHUFRISAT
Events for September 28, 2022
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Medical Imaging Seminar Series
Wed, Sep 28, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rodrigo A. Lobos, Electrical and Computer Engineering, University of Southern California
Talk Title: New Theory and Methods for Accelerated MRI Reconstruction
Series: Medical Imaging Seminar Series
Abstract: Magnetic resonance imaging (MRI) has revolutionized medicine by providing high-quality images of living tissue in a safe and noninvasive manner. However, the data-acquisition time can still be restrictively long in real applications. To accelerate this task, one popular alternative has been acquiring a reduced amount of data samples, and then using reconstruction methods to generate images out of the acquired undersampled data. In this talk we discuss novel contributions to improve the performance and efficiency of MRI reconstruction methods.
We start revisiting the shift-invariant linear predictability relationships that exist in the MRI data (k-space), and how they can be leveraged using structured low-rank modeling (SLM). Then, we propose novel reconstruction approaches based on SLM which additionally incorporate in-prior knowledge learned from previously acquired reference data. We show that this approach is particularly useful in the context of ghost-artifact correction in echo planar imaging (EPI), where we theoretically establish that in-prior knowledge is necessary in order to avoid ill-posedness when using SLM reconstruction methods. Next, we provide a robust and powerful SLM reconstruction method able to account for potential imperfections in the reference data.
In the last part of the talk, we show that linear predictability principles can also be used in the context of sensitivity map estimation in multichannel MRI. We start showing new theoretical results that provide a novel mathematical description for the estimation problem. Specifically, we show that sensitivity maps at particular locations belong to a nullspace of a matrix created from linear predictability relationships. Then, based on advanced signal processing techniques, we propose a set of computational methods which allow massive improvements in the computational complexity of sensitivity map estimation methods based on subspaces. We show cases where conventional estimation methods obtain a ~30-fold acceleration when combined with our proposed computational techniques. Notably, these improvements in computational time and memory usage are obtained without sacrificing estimation accuracy.
Biography: Rodrigo A. Lobos is a Ph.D candidate in Electrical and Computer Engineering at University of Southern California, supervised by Prof. Justin Haldar. He obtained his Bachelor's and Master's degree in Electrical Engineering at Universidad de Chile, where he received the Best Master's Thesis award in Electrical Engineering in 2015. During this time, The School of Engineers of Chile recognized Rodrigo as the best electrical engineer graduated from Universidad de Chile in 2015. He then joined Prof. Haldar's group at USC where his research has been focused on signal processing, computational imaging, and machine learning applied to medical imaging applications. Rodrigo's work has been recognized in distinguished medical imaging conferences, where he obtained a Best Paper Finalist award in IEEE ISBI 2020. At University of Southern California Rodrigo was selected as a Ming Hsieh Institute Ph.D Scholar.
Host: Justin Haldar, jhaldar@usc.edu
Webcast: https://urldefense.com/v3/__https://usc.zoom.us/j/94607557250__;!!LIr3w8kk_Xxm!uZV7rWNY9SZv84hGG8xVjIzaW-bOpw5wrC274dcH8O-_Ls5VS_GnF-W-kPDxVNU489rUSCih4KKPsjXwog$Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
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New Theory and Methods for Accelerated MRI Reconstruction
Wed, Sep 28, 2022 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rodrigo Lobos , Electrical and Computer Engineering
Talk Title: Dissertation Defense
Host: Rodrigo Lobos
More Information: Rodrigo Lobos_MHI-MISS_Sept. 28, 2022.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Sep 28, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Giulia Pedrielli, School of Computing and Augmented Intelligence (SCAI) at Arizona State University.
Talk Title: Going Inside the Box: Bayesian Optimization for Verification of Cyber Physical Systems with Varying Levels of System Knowledge
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Systems across automotive, bio-pharma, aerospace, energy, have become increasingly complex, and simulation represents a standard tool to evaluate their performance independently from the purpose of the analysis being optimization, control, certification. As a result, black-box optimization, that can embed simulation to perform a wide range of analyses, has attracted a lot of attention from the science and engineering communities. This talk centers around Black-box optimization methods, focusing on random search approaches (such randomness is injected in the search independently from the problem being affected by noise) in the broad area of verification of Cyber Physical Systems. In this context, the problem of falsifying properties is translated into the minimization of a robustness function. This is a metric function that quantifies how far a CPS execution is from violating a property of interest.
We first focus on control and acceleration of the explore/exploit process for the falsification of safety requirements without exploiting any property of the system under analysis. Our approach alternates local and global search using local knowledge while exploring the space of possible solutions. The performance of the proposed approach is analyzed, and key future directions are discussed in the context of Cyberphysical systems safety evaluation.
In the second part of the talk, we present algorithms developed in the scope of certification of safety critical systems that in some form exploit some structure of the problem at hand. Part-X is a family of partitioning informed Bayesian optimizers that can identify regions in which the system can present safety concerns (bugs in the case a software is analyzed). In this sense, the algorithm learns structure of the robustness function used to find falsification. We also produce a global estimate of the falsification volume. The algorithm min-BO works to identify faults in systems that have complex requirements that can be decomposed into a set of simpler requirements that need to be simultaneously satisfied by the system (conjunctive requirements). Finally, we show the basic ideas behind the design of algorithms that can exploit, when available, instrumented source code for the CPS to verify.
Biography: Giulia Pedrielli (https://www.gpedriel.com/) is currently Associate Professor for the School of Computing and Augmented Intelligence (SCAI) at Arizona State University. She graduated from the Department of Mechanical Engineering of Politecnico di Milano. Giulia develops her research in design and analysis of random algorithms for global optimization, with focus on improving finite time performance and scalability of these approaches. Her work is motivated by design and control of next generation manufacturing systems in bio-pharma and aerospace applications, as well as problems in the design and evaluation of complex molecular structures in life-science. Applications of her work are in individualized cancer care, bio-manufacturing, design and control of self-assembled RNA structures, verification of Cyberphysical systems. Her research is funded by the NSF, DHS, DARPA, Intel, Lockheed Martin.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/98083929768?pwd=SUJreHk0N0ZXbk5QZ1ZPUkRlM3FmZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/j/98083929768?pwd=SUJreHk0N0ZXbk5QZ1ZPUkRlM3FmZz09
Audiences: Everyone Is Invited
Contact: Talyia White
