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Neural operator learning as a means of mapping between complex functionspaces has garnered significant attention in the field of computational scienceand engineering (CS&E). In this paper, we apply Neural operator learning to thetime-of-flight ultrasound computed tomography (USCT) problem. We learn themapping between time-of-flight (TOF) data and the heterogeneous sound speedfield using a full-wave solver to generate the training data. This novelapplication of operator learning circumnavigates the need to solve thecomputationally intensive iterative inverse problem. The operator learns thenon-linear mapping offline and predicts the heterogeneous sound field with asingle forward pass through the model. This is the first time operator learninghas been used for ultrasound tomography and is the first step in potentialreal-time predictions of soft tissue distribution for tumor identification inbeast imaging.

Neural Operator Learning for Ultrasound Tomography Inversion