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 data sets, 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.

中文翻译：

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人工神经网络在声学反演中的模型简化

在超声层析成像中，根据对象周围传感器进行的声学测量来估计对象内部的声速。精确的正向模型是高质量图像重建的重要因素，但是在许多应用中，它会使计算变得非常耗时。使用近似正向模型，可以加快计算速度，但是重建的质量可能会受到影响。本文提出了一种基于神经网络的方法，该方法可以补偿由近似正向模型引起的建模误差。该方法已在模拟的二维域中使用各种不同的成像方案进行了测试。结果表明，在训练数据集较小的情况下，该方法可用于近似建模误差，