Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by providing input data that is as rich as the output data, e.g., image sequence plus conditioning in, video out. However, clinical documentation is usually scarce and only single images are reported and stored, thus retrospective patient-specific analysis or the generation of rich training data becomes impossible with current approaches. In this paper, we extend elucidated diffusion models for video modelling to generate plausible video sequences from single images and arbitrary conditioning with clinical parameters. We explore this idea within the context of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We use the publicly available EchoNet-Dynamic dataset for all our experiments. Our image to sequence approach achieves an $R^2$ score of 93%, which is 38 points higher than recently proposed sequence to sequence generation methods. Code and models will be available at: https://github.com/HReynaud/EchoDiffusion.

翻译：图像合成有望为机器学习方法在临床实践中的转化提供价值。模型鲁棒性、领域迁移、因果建模和操作员培训等基本问题可通过合成数据得以解决。尤其是超声成像等高度依赖操作员的模态，需要稳健的图像与视频生成框架。到目前为止，视频生成仅能通过提供与输出数据同样丰富的输入数据实现（例如，图像序列加条件输入、视频输出）。然而，临床文档通常稀缺，仅记录和存储单张图像，因此当前方法无法进行回顾性患者特异性分析或生成丰富的训练数据。在本文中，我们将阐明用于视频建模的扩散模型扩展为从单张图像和任意临床参数条件生成合理的视频序列。我们通过研究左心室射血分数（这些检查中获得的最关键临床指标）的变化，在超声心动图背景下探索这一思路。所有实验均使用公开的EchoNet-Dynamic数据集。我们的图像到序列方法实现了93%的R²分数，比最近提出的序列到序列生成方法高出38个百分点。代码和模型将在以下网址提供：https://github.com/HReynaud/EchoDiffusion。