DGIST (Daegu Gyeongbuk Institute of Science and Technology)
□ DGIST (President: Kuk Yang) Department of Electrical Engineering and Computer Science Professor Hwang Jae-yoon's team developed a 'deep learning-based ultrasound hologram generation framework' technology that can freely configure the form of focused ultrasound in real time based on holograms. It is expected to be used as a basic technology in the field of brain stimulation and treatment that requires precision in the future.
□ Ultrasound is a safe technology even used for prenatal examination. Since it can stimulate deep areas without surgery, ultrasound methods for brain stimulation and treatment have recently been studied, and results that ultrasound brain stimulation have actually improved diseases, such as Alzheimer's disease, depression, and pain, have been published.
□ However, the problem is that it is difficult to selectively stimulate related areas of the brain in which several areas interact with each other at the same time because the current technology focuses ultrasound into a single small point or a large circle for stimulation. To solve this problem, a technology capable of freely focusing ultrasound on a desired area using the hologram principle had been proposed, but has limitations, such as low accuracy and long calculation time to generate a hologram.
□ DGIST Professor Hwang Jae-yoon's team proposed a deep learning-based learning framework that can embody free and accurate ultrasound focusing in real time by learning to generate ultrasound holograms to overcome the limitations. As a result, Professor Hwang's team demonstrated that it was possible to focus ultrasound into the desired form more accurately in a hologram creation time close to real time, and maximum 400 times faster than the existing ultrasound hologram generation algorithm method.
□ The deep learning-based learning framework proposed by the research team learns to generate ultrasonic holograms through self-supervised learning. Self-supervised learning is a method of learning to find the answer by finding a rule on its own for data with no answer. The research team proposed a methodology for learning to generate ultrasonic hologram, a deep learning network optimized for ultrasonic hologram generation, and a new loss function, while proving the validity and excellence of each component through simulations and actual experiments.
□ DGIST Department of Electrical Engineering and Computer Science Professor Hwang Jae-yoon said, “We applied deep learning technology to ultrasound holograms proposed relatively recently. As a result, we developed a technology that can freely, quickly and accurately generate and change the form of ultrasound beams,” and added, “We hope that the results of this research are used in patient-specific precision brain stimulation technology and general ultrasound fields (ultrasound imaging, thermal therapy, etc.).”
□ Meanwhile, this research was carried out with the Ministry of Science and ICT’s Four Major Institutes of Science and Technology Support Program. Researcher Lee Moon-hwan of DGIST Information and Communication Engineering Research Center, Ph.D. students Ryu Ha-min and Yoon Sang-yeon of the Department of Electrical Engineering and Computer Science, and GIST Professor Kim Tae’s team. The research results were published as a cover paper in the December edition of ‘IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control’, an international academic journal in the related field.
Correspondent author's email address : [email protected]
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control
10.1109/TUFFC.2022.3219401
Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation
4-Nov-2022
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Media Contact
Kwanghoon CHOI
DGIST (Daegu Gyeongbuk Institute of Science and Technology)
[email protected]
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DGIST (Daegu Gyeongbuk Institute of Science and Technology)
Copyright © 2022 by the American Association for the Advancement of Science (AAAS)
Copyright © 2022 by the American Association for the Advancement of Science (AAAS)
