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- W4363671593 abstract "Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In this paper, we apply Neural operator learning to the time-of-flight ultrasound computed tomography (USCT) problem. We learn the mapping between time-of-flight (TOF) data and the heterogeneous sound speed field using a full-wave solver to generate the training data. This novel application of operator learning circumnavigates the need to solve the computationally intensive iterative inverse problem. The operator learns the non-linear mapping offline and predicts the heterogeneous sound field with a single forward pass through the model. This is the first time operator learning has been used for ultrasound tomography and is the first step in potential real-time predictions of soft tissue distribution for tumor identification in beast imaging." @default.
- W4363671593 created "2023-04-11" @default.
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- W4363671593 date "2023-04-06" @default.
- W4363671593 modified "2023-09-26" @default.
- W4363671593 title "Neural Operator Learning for Ultrasound Tomography Inversion" @default.
- W4363671593 doi "https://doi.org/10.48550/arxiv.2304.03297" @default.
- W4363671593 hasPublicationYear "2023" @default.
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