Ultrasound (US) imaging is indispensable in clinical practice. To diagnose certain diseases, sonographers must observe corresponding dynamic anatomic structures to gather comprehensive information. However, the limited availability of specific US video cases causes teaching difficulties in identifying corresponding diseases, which potentially impacts the detection rate of such cases. The synthesis of US videos may represent a promising solution to this issue. Nevertheless, it is challenging to accurately animate the intricate motion of dynamic anatomic structures while preserving image fidelity. To address this, we present a novel online feature-decoupling framework called OnUVS for high-fidelity US video synthesis. Our highlights can be summarized by four aspects. First, we introduced anatomic information into keypoint learning through a weakly-supervised training strategy, resulting in improved preservation of anatomical integrity and motion while minimizing the labeling burden. Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator. Third, we adopted a multiple-feature discriminator to extract a comprehensive range of visual cues, thereby enhancing the sharpness and fine details of the generated videos. Fourth, we constrained the motion trajectories of keypoints during online learning to enhance the fluidity of generated videos. Our validation and user studies on in-house echocardiographic and pelvic floor US videos showed that OnUVS synthesizes US videos with high fidelity.

翻译：超声成像在临床实践中不可或缺。为了诊断某些疾病，超声医师必须观察相应的动态解剖结构以获取全面信息。然而，特定超声视频病例的有限可用性导致在识别相应疾病时存在教学困难，这可能影响此类病例的检出率。超声视频合成可能是解决该问题的一种有前景的方案。然而，在保持图像保真度的同时精确模拟动态解剖结构的复杂运动极具挑战性。为此，我们提出了一种新颖的在线特征解耦框架OnUVS，用于高保真超声视频合成。本工作的亮点可归纳为四个方面。首先，我们通过弱监督训练策略将解剖信息引入关键点学习，从而在最小化标注负担的同时，更好地保留解剖完整性与运动特征。其次，为了更好地保持超声图像的完整性与纹理信息，我们在生成器中采用双解码器机制，将内容特征与纹理特征进行解耦。第三，我们采用多特征判别器提取全面的视觉线索，从而增强生成视频的清晰度与精细细节。第四，我们在在线学习过程中约束关键点的运动轨迹，以提升生成视频的流畅性。针对院内超声心动图与盆底超声视频的验证与用户研究表明，OnUVS能够合成高保真度的超声视频。