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Abstract
The success of diagnostic and interventional medical procedures is deeply rooted in the ability of modern imaging systems to deliver clear and interpretable information. After raw sensor data is received by ultrasound and photoacoustic imaging systems in particular, the beamforming process is often the first line of software defense against poor quality images. Yet, with today's state-of-the-art beamformers, ultrasound and photoacoustic images remain challenged by channel noise, reflection artifacts, and acoustic clutter, which combine to complicate segmentation tasks and confuse overall image interpretation. These challenges exist because traditional beamforming and image formation steps are based on flawed assumptions in the presence of significant inter- and intrapatient variations.
In this talk, I will introduce the PULSE Lab's novel alternative to beamforming, which improves ultrasound and photoacoustic image quality by learning from the physics of sound wave propagation. We replace traditional beamforming steps with deep neural networks that only display segmented details, structures, and physical properties of interest. I will then transition to describing a new resource for the entire community to standardize and accelerate research at the intersection of ultrasound beamforming and deep learning. This resource is a direct outcome of the 2020 Challenge on Ultrasound Beamforming with Deep Learning, with key landmarks that include the first internationally crowd-sourced database of raw ultrasound channel data and integrated beamforming and evaluation code.
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Bio
Dr. Muyinatu A. Lediju Bell (informally known as "Bisi") is the John C. Malone Associate Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science at Johns Hopkins University. Dr. Bell earned a B.S. degree in Mechanical Engineering (biomedical engineering minor) from Massachusetts Institute of Technology, received a Ph.D. degree in Biomedical Engineering from Duke University, conducted research abroad as a Whitaker International Fellow at the Institute of Cancer Research and Royal Marsden Hospital in the United Kingdom, and completed a postdoctoral fellowship with the Engineering Research Center for Computer-Integrated Surgical Systems and Technology at Johns Hopkins University. She has published over 140 scientific journal articles and conference papers, delivered over 85 invited talks (including keynote and plenary presentations), and holds patent for SLSC beamforming, photoacoustic-guided surgery, and deep learning for beamforming. Dr. Bell is the recipient of numerous awards, grants, and fellowships, including the NIH K99/R00 Pathway to Independence Award, MIT Technology Review's Innovator Under 35 Award, the NSF CAREER Award, the NIH Trailblazer Award, the Alfred P. Sloan Research Fellowship, Maryland's Outstanding Young Engineer Award, the ORAU Ralph E. Powe Junior Faculty Enhancement Award, and the SPIE Early Career Achievement Award. She was elected and inducted as a 2022 Fellow of AIMBE, one of only four out of 1,500 in the preceding decade to receive this honor as an assistant professor.
Dr. Bell leads a highly interdisciplinary research program that integrates optics, acoustics, robotics, electronics, and mechanics, as well as signal processing and medical device design, to engineer and deploy innovative biomedical imaging systems that simultaneously address unmet clinical needs and significantly improve the standard of patient care. As the director of the Photoacoustic and Ultrasonic Systems Engineering (PULSE) Lab, Dr. Bell develops theories, models, and simulations to investigate advanced beamforming techniques for improving ultrasonic and photoacoustic image quality. In parallel, she designs and builds novel light delivery systems for photoacoustic imaging and incorporates medical robots to improve operator maneuverability and enable standardized procedures for more personalized medicine. The technologies developed in her lab are then interfaced with patients to facilitate clinical translation. These technologies have applications in neurosurgical navigation, cardiovascular disease, women's health, and cancer detection and treatment.
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