Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction.

Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for application of deep learning methods to practical ulrasound tomography image reconstruction based on simulation dataset.