Modeling a Multi-Element Ultrasound Transducer via Component-Focused Physics-informed Neural Networks

Shaikhah Alkhadhr, Mohamed Almekkawy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The accurate modeling of wavefields is a crucial aspect of ultrasound therapeutics. Physics-Informed Neural Networks (PINNs) are recognized as potent tools for solving Partial Differential Equations (PDEs) in ultrasound modeling. However, similar to other deep learning methods, PINNs face challenges in training, and the oscillatory nature of the Linear Wave Equation (LWE) adds to their complexity. Additionally, the presence of a forcing time-dependent source term in the LWE necessitates careful consideration when training PINNs to avoid erroneous predictions. This study aims to enhance the accuracy and convergence of ultrasound wavefield modeling, specifically for a multielement focused ultrasound transducer (FUST), through an innovative component-focused PINN architecture. This architecture integrates both Convolutional Neural Networks (CNN) and Feed-forward Neural Networks (FNN) as approximator networks. Our approach separates attention from the different terms of LWE. By employing a combination of CNN and FNN within the PINN architecture, the model focuses separately on the components of the LWE. This approach does not require previously known solutions as the training data. When using the Finite Difference Time Domain (FDTD) method as the true solution, the proposed component-focused PINN architecture demonstrates a significant reduction in L2 relative error (L2RE) compared to PINN models using standard fully-connected FNNs. Additionally, our model exhibits faster convergence, which contributes to increased efficiency in ultrasound wavefield modeling. This innovative approach has potential implications for the advancement of modeling using deep-learning methods in ultrasound therapeutics.

Original languageEnglish (US)
Title of host publicationIUS 2023 - IEEE International Ultrasonics Symposium, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350346459
DOIs
StatePublished - 2023
Event2023 IEEE International Ultrasonics Symposium, IUS 2023 - Montreal, Canada
Duration: Sep 3 2023Sep 8 2023

Publication series

NameIEEE International Ultrasonics Symposium, IUS
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2023 IEEE International Ultrasonics Symposium, IUS 2023
Country/TerritoryCanada
CityMontreal
Period9/3/239/8/23

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

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