Abstract: Purpose. Given the high level of expertise required for navigation and
interpretation of ultrasound images, computational simulations can facilitate
the training of such skills in virtual reality. With ray-tracing based
simulations, realistic ultrasound images can be generated. However, due to
computational constraints for interactivity, image quality typically needs to
Methods. We propose herein to bypass any rendering and simulation process at
interactive time, by conducting such simulations during a non-time-critical
offline stage and then learning image translation from cross-sectional model
slices to such simulated frames. We use a generative adversarial framework with
a dedicated generator architecture and input feeding scheme, which both
substantially 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 input
maps to intermediate layers in order to preserve locality are all shown herein
to greatly facilitate such translation task.
Results. Given several quality metrics, the proposed method with only tissue
maps as input is shown to provide comparable or superior results to a
state-of-the-art that uses additional images of low-quality ultrasound
renderings. An extensive ablation study shows the need and benefits from the
individual contributions utilized in this work, based on qualitative examples
and quantitative ultrasound similarity metrics. To that end, a local histogram
statistics based error metric is proposed and demonstrated for visualization of
local dissimilarities between ultrasound images.