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arXiv · 2304.03297

Neural Operator Learning for Ultrasound Tomography Inversion

Haocheng Dai·Michael Penwarden·Robert M. Kirby·Sarang Joshi
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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.

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Haocheng Dai, Michael Penwarden, Robert M. Kirby, Sarang Joshi. 2023-04-06. Neural Operator Learning for Ultrasound Tomography Inversion. https://arxiv.org/abs/2304.03297

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