@inproceedings{8abfa17410ad403180a5382ad0d51302,
title = "Full Wave Inversion For Ultrasound Tomography Using Physics Based Deep Neural Network",
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.",
author = "Xilun Liu and Mohamed Almekkawy",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230537",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "United States",
}