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.

本文介绍了一种新的应用神经操作学习的方法用于解决超声计算机断层扫描中的时飞行问题，通过学习 time-of-flight 数据和声速场之间复杂的非线性映射来实现对异质声速场的预测，从而实现对肿瘤识别中软组织分布的实时预测。