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Purpose. Given the high level of expertise required for navigation andinterpretation of ultrasound images, computational simulations can facilitatethe training of such skills in virtual reality. With ray-tracing basedsimulations, realistic ultrasound images can be generated. However, due tocomputational constraints for interactivity, image quality typically needs tobe compromised.  Methods. We propose herein to bypass any rendering and simulation process atinteractive time, by conducting such simulations during a non-time-criticaloffline stage and then learning image translation from cross-sectional modelslices to such simulated frames. We use a generative adversarial framework witha dedicated generator architecture and input feeding scheme, which bothsubstantially improve image quality without increase in network parameters.Integral attenuation maps derived from cross-sectional model slices,texture-friendly strided convolutions, providing stochastic noise and inputmaps to intermediate layers in order to preserve locality are all shown hereinto greatly facilitate such translation task.  Results. Given several quality metrics, the proposed method with only tissuemaps as input is shown to provide comparable or superior results to astate-of-the-art that uses additional images of low-quality ultrasoundrenderings. An extensive ablation study shows the need and benefits from theindividual contributions utilized in this work, based on qualitative examplesand quantitative ultrasound similarity metrics. To that end, a local histogramstatistics based error metric is proposed and demonstrated for visualization oflocal dissimilarities between ultrasound images.

Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training