  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.
