Neural Operator Learning for Ultrasound Tomography Inversion
This enables potential real-time tumor identification in breast imaging, but it is an incremental step as it applies an existing method to a new domain.
The paper tackled the ultrasound computed tomography inversion problem by applying neural operator learning to map time-of-flight data to sound speed fields, achieving predictions with a single forward pass and bypassing iterative computations.
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.