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向临床实践的转化提供价值。诸如模型鲁棒性、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到解决。特别是重度依赖操作员的成像模态（如超声成像）需要鲁棒的图像和视频生成框架。迄今为止，视频生成仅能在输入数据与输出数据同等丰富的情况下实现，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，仅报告和存储单张图像，因此当前方法无法实现回顾性患者特异性分析或生成丰富的训练数据。在本文中，我们将去噪扩散模型扩展至视频建模领域，以从单张图像和任意临床参数条件中生成合理的视频序列。我们通过分析左心室射血分数（超声检查中最关键的临床指标）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，比近期提出的序列到序列生成方法高出38个百分点。代码和模型将发布于：https://github.com/HReynaud/EchoDiffusion。