EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesis

TL;DR

EchoLVFM introduces a fast, one-step latent flow-matching model for controllable, high-fidelity echocardiogram video synthesis, enabling efficient data augmentation and clinical parameter control.

Contribution

It presents a novel one-step latent flow-matching framework that significantly improves sampling efficiency while maintaining visual quality and controllability over clinical parameters.

Findings

Achieves ~50x faster sampling than multi-step methods.

Maintains high visual fidelity and clinical parameter control.

Expert clinicians' assessment shows near-chance discrimination accuracy.

Abstract

Echocardiography is widely used for assessing cardiac function, where clinically meaningful parameters such as left-ventricular ejection fraction (EF) play a central role in diagnosis and management. Generative models capable of synthesising realistic echocardiogram videos with explicit control over such parameters are valuable for data augmentation, counterfactual analysis, and specialist training. However, existing approaches typically rely on computationally expensive multi-step sampling and aggressive temporal normalisation, limiting efficiency and applicability to heterogeneous real-world data. We introduce EchoLVFM, a one-step latent video flow-matching framework for controllable echocardiogram generation. Operating in the latent space, EchoLVFM synthesises temporally coherent videos in a single inference step, achieving a $\mathbf{\sim 50\times}$ improvement in sampling efficiency compared to multi-step flow baselines while maintaining visual fidelity. The model supports global conditioning on clinical variables, demonstrated through precise control of EF, and enables reconstruction and counterfactual generation from partially observed sequences. A masked conditioning strategy further removes fixed-length constraints, allowing shorter sequences to be retained rather than discarded. We evaluate EchoLVFM on the CAMUS dataset under challenging single-frame conditioning. Quantitative and qualitative results demonstrate competitive video quality, strong EF adherence, and 57.9% discrimination accuracy by expert clinicians which is close to chance. These findings indicate that efficient, one-step flow matching can enable practical, controllable echocardiogram video synthesis without sacrificing fidelity. Code available at: https://github.com/EngEmmanuel/EchoLVFM