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.
@article{arxiv.2304.03297,
title = {Neural Operator Learning for Ultrasound Tomography Inversion},
author = {Haocheng Dai and Michael Penwarden and Robert M. Kirby and Sarang Joshi},
journal= {arXiv preprint arXiv:2304.03297},
year = {2023}
}