Full Wave Inversion For Ultrasound Tomography Using Physics Based Deep Neural Network

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

Abstract

Ultrasound Computed Tomography (USCT) is a promising imaging modality for reconstructing tissue properties. Full-wave inversion (FWI) provides an enhanced contrast image. In this paper, we introduce an optimized Physics-Informed Neural Network (PINN) architecture to solve the inverse problems of FWI. PINNs are neural networks that can approximate universal functions using partial differential equations that govern the physics of the system. When training the network, the deep neural network can converge to learn the solutions of the wave equation by minimizing the loss function. The results showed that our proposed networks can retrieve the Speed of Sound (SoS) information and significantly decrease the computational cost via the transfer learning technique.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period4/18/234/21/23

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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