  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.
