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 be compromised. 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.
翻译:鉴于对超声波图像的导航和判读需要高水平的专门知识,计算模拟可以促进虚拟现实中这类技能的培训。通过光谱模拟,可以产生现实超声波图像。然而,由于互动的计算限制,图像质量通常需要降低。我们在此建议,在非时间临界离线阶段进行这种模拟,从而绕过任何图像和模拟过程,然后从跨部门模型切片到模拟框架学习图像翻译。我们使用一个配有专用发电机结构和投入进料喂养计划的基因化对抗框架,既能大大改善图像质量,又不会增加网络参数。由于截面模型切片的计算限制,图像质量综合加固图,便于素质化,向中间层提供声调和输入图解图解图解图解,以保存地点,从而大大便利翻译任务。根据若干高质量的衡量标准,只将组织图解为输入提供可比较或优异效果,以便向州提供可比较或更佳的图像,同时不增加网络参数参数参数参数。从跨部门模型中得出更多、超质量统计数据。