  In ultrasound tomography, the speed of sound inside an object is estimated
based on acoustic measurements carried out by sensors surrounding the object.
An accurate forward model is a prominent factor for high-quality image
reconstruction, but it can make computations far too time-consuming in many
applications. Using approximate forward models, it is possible to speed up the
computations, but the quality of the reconstruction may have to be compromised.
In this paper, a neural network -based approach is proposed, that can
compensate for modeling errors caused by the approximate forward models. The
approach is tested with various different imaging scenarios in a simulated
two-dimensional domain. The results show that with fairly small training
datasets, the proposed approach can be utilized to approximate the modelling
errors, and to significantly improve the image reconstruction quality in
ultrasound tomography, compared to commonly used inversion algorithms.
