Neural Operator Learning for Ultrasound Tomography Inversion
- URL: http://arxiv.org/abs/2304.03297v2
- Date: Sun, 28 May 2023 04:43:17 GMT
- Title: Neural Operator Learning for Ultrasound Tomography Inversion
- Authors: Haocheng Dai, Michael Penwarden, Robert M. Kirby, Sarang Joshi
- Abstract summary: We learn the mapping between time-of-flight (TOF) data and the heterogeneous sound speed field using a full-wave solver.
This is the first time operator learning has been used for ultrasound tomography.
- Score: 4.759011874234158
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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