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M. Jang¹, M. Choi¹, I. Jeong¹, S.S Yoo², K. Yoon³ and G. Noh¹

¹School of Mechanical Engineering, Korea University, Seoul, South Korea
²Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
²School of Mathematics and Computing, Yonsei University, Seoul, South Korea

doi:10.4203/ccc.5.4.3

M. Jang, M. Choi, I. Jeong, S.S Yoo, K. Yoon, G. Noh, "Deep Learning Approach to Predict Acoustic Field in Transcranial Focused Ultrasound", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 4.3, 2023, doi:10.4203/ccc.5.4.3

Keywords: deep learning, surrogate model, transcranial focused ultrasound, finite difference time-domain, real-time.

Abstract

The distortion caused by the skull poses challenges in determining the intensity and focal position of ultrasound waves in transcranial focused ultrasound therapy. Computational simulations can address this but are limited by high computational costs. To overcome this, we propose a deep learning-based surrogate model that provides real-time generation of the focal position and distribution of ultrasound waves passing through the skull. The model is trained using data from computational simulations performed on multiple skulls, allowing it to reflect distortion characteristics and predict acoustic fields for any skull. The proposed model offers a promising solution for accurate and efficient real-time estimation of the intracranial acoustic field.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 5 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, MACHINE LEARNING AND OPTIMISATION IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: P. Iványi, J. Logo and B.H.V. Topping Paper 4.3 Deep Learning Approach to Predict Acoustic Field in Transcranial Focused Ultrasound M. Jang¹, M. Choi¹, I. Jeong¹, S.S Yoo², K. Yoon³ and G. Noh¹ ¹School of Mechanical Engineering, Korea University, Seoul, South Korea ²Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA ²School of Mathematics and Computing, Yonsei University, Seoul, South Korea doi:10.4203/ccc.5.4.3 Full Bibliographic Reference for this paper M. Jang, M. Choi, I. Jeong, S.S Yoo, K. Yoon, G. Noh, "Deep Learning Approach to Predict Acoustic Field in Transcranial Focused Ultrasound", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 4.3, 2023, doi:10.4203/ccc.5.4.3 Keywords: deep learning, surrogate model, transcranial focused ultrasound, finite difference time-domain, real-time. Abstract The distortion caused by the skull poses challenges in determining the intensity and focal position of ultrasound waves in transcranial focused ultrasound therapy. Computational simulations can address this but are limited by high computational costs. To overcome this, we propose a deep learning-based surrogate model that provides real-time generation of the focal position and distribution of ultrasound waves passing through the skull. The model is trained using data from computational simulations performed on multiple skulls, allowing it to reflect distortion characteristics and predict acoustic fields for any skull. The proposed model offers a promising solution for accurate and efficient real-time estimation of the intracranial acoustic field. download the full-text of this paper (PDF, 7 pages, 504 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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