# Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis

arXiv: https://arxiv.org/abs/2303.12644 · HTML: https://mlsanity.com/paper/2303.12644/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 27 of 27)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [Phenaki: Variable Length Video Generation From Open Domain Textual Description](https://mlsanity.com/paper/2210.02399/) (2022)
- [Unsupervised Learning for Physical Interaction through Video Prediction](https://mlsanity.com/paper/1605.07157/) (2016)
- [Diffusion Probabilistic Modeling for Video Generation](https://mlsanity.com/paper/2203.09481/) (2022)
- [Stochastic Variational Video Prediction](https://mlsanity.com/paper/1710.11252/) (2017)
- [MaskViT: Masked Visual Pre-Training for Video Prediction](https://mlsanity.com/paper/2206.11894/) (2022)
- [Video Pixel Networks](https://mlsanity.com/paper/1610.00527/) (2016)
- [FitVid: Overfitting in Pixel-Level Video Prediction](https://mlsanity.com/paper/2106.13195/) (2021)
- [Talking Face Generation by Conditional Recurrent Adversarial Network](https://mlsanity.com/paper/1804.04786/) (2018)
- [VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation](https://mlsanity.com/paper/1903.01434/) (2019)
- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [D'ARTAGNAN: Counterfactual Video Generation](https://mlsanity.com/paper/2206.01651/) (2022)
- [EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks](https://mlsanity.com/paper/2208.14003/) (2022)
- [Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images](https://mlsanity.com/paper/2103.05745/) (2021)
- [Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling](https://mlsanity.com/paper/2207.00474/) (2022)

## Built on by (most cited first, 30 of 32)

- [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023)
- [EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing](https://mlsanity.com/paper/2406.00808/) (2024)
- [Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models](https://mlsanity.com/paper/2503.14966/) (2025)
- [ECHOPulse: ECG controlled echocardio-grams video generation](https://mlsanity.com/paper/2410.03143/) (2024)
- [Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability](https://mlsanity.com/paper/2303.17908/) (2023)
- [Interactive Generation of Laparoscopic Videos with Diffusion Models](https://mlsanity.com/paper/2406.06537/) (2024)
- [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025)
- [Echocardiography video synthesis from end diastolic semantic map via diffusion model](https://mlsanity.com/paper/2310.07131/) (2023)
- [Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks](https://mlsanity.com/paper/2403.19880/) (2024)
- [Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis](https://mlsanity.com/paper/2408.03035/) (2024)
- [EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation](https://mlsanity.com/paper/2503.22357/) (2025)
- [URCDM: Ultra-Resolution Image Synthesis in Histopathology](https://mlsanity.com/paper/2407.13277/) (2024)
- [Explainable and Controllable Motion Curve Guided Cardiac Ultrasound Video Generation](https://mlsanity.com/paper/2407.21490/) (2024)
- [Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification](https://mlsanity.com/paper/2409.17091/) (2024)
- [Counterfactual Probabilistic Diffusion with Expert Models](https://mlsanity.com/paper/2508.13355/) (2025)
- [Enabling PSO-Secure Synthetic Data Sharing Using Diversity-Aware Diffusion Models](https://mlsanity.com/paper/2506.17975/) (2025)
- [EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesis](https://mlsanity.com/paper/2603.13967/) (2026)
- [Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identification](https://mlsanity.com/paper/2411.04956/) (2024)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis: Advancing Pretraining and Clinical Applications](https://mlsanity.com/paper/2505.03426/) (2025)
- [From Transthoracic to Transesophageal: Cross-Modality Generation using LoRA Diffusion](https://mlsanity.com/paper/2508.13077/) (2025)
- [EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data](https://mlsanity.com/paper/2409.07566/) (2024)
- [LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations](https://mlsanity.com/paper/2509.08422/) (2025)
- [Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generation](https://mlsanity.com/paper/2311.01567/) (2023)
- [Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology](https://mlsanity.com/paper/2312.01152/) (2023)
- [An Audio-textual Diffusion Model For Converting Speech Signals Into Ultrasound Tongue Imaging Data](https://mlsanity.com/paper/2403.05820/) (2024)
- [Echo-Path: Pathology-Conditioned Echo Video Generation](https://mlsanity.com/paper/2509.17190/) (2025)
- [Graph Conditioned Diffusion for Controllable Histopathology Image Generation](https://mlsanity.com/paper/2510.07129/) (2025)
- [Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis](https://mlsanity.com/paper/2512.09418/) (2025)
- [InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography](https://mlsanity.com/paper/2512.09422/) (2025)

## Abstract (arXiv)

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 R2 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.