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

Emmanuel Oladokun; Sarina Thomas; Jurica Šprem; Vicente Grau

eess.IVMar 14, 2026

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

Authors: Emmanuel Oladokun, Sarina Thomas, Jurica Šprem, Vicente Grau

Organizations: University of Oxford, Oxford, England · GE HealthCare, Cardiovascular Ultrasound R&D, Oslo, Norway

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

Figures & tables

Figure 1: EchoLVFM. Training: Videos are noised and processed in latent space. All but one observed frame are zeroed to form $x_{m}$ , which serves as conditioning. To support variable-length sequences, a padding vector $p$ indicates which frames are valid observations in the temporally augmented input. The target EF $\phi$ is provided as global conditioning. $r$ and $t$ denote timesteps with $r<t$ , and the model $u_{\theta}$ learns to predict the conditional average velocity over the interval $[r,t]$ . Inference: A partial video containing as little as a single observed frame, together with a target EF and random noise, is passed to the trained model. One-step integration produces the generated video.

				Video Quality				EF Adherence
Method	Cond.	Steps (Vid/ $s$ )	Task	FID $\downarrow$	FVD 10 $\downarrow$	SSIM $\uparrow$	LPIPS $\downarrow$	$R^{2}$ , $\%$ $\uparrow$ $\mu$ (RS)	MAE, $\%$ $\downarrow$ $\mu$ (RS)	RMSE, $\%$ $\downarrow$ $\mu$ (RS)
HeartBeat 2C [ 31 ]	I, T	1000 †	-	36.3	9.6 ‡	0.61	-
HeartBeat 4C [ 31 ]	I, T	1000 †	-	36.0	12.6 ‡	0.61	-
CES [ 13 ]	MM,V	1000	-	26.6	69.6 ‡	0.57	-
			Rec	40.4	153.9	0.73	0.128	73 (84)	5.7 (4.0)	7.3 (5.6)
Linear	I, EF	25 (0.37)	Gen	42.2	154.2	-	-	59 (67)	14.4 (12.2)	18.6 (16.8)

Table 1: Quantitative Results. Comparison of baseline and proposed methods. Cond. lists the inputs: Image (I) or single frame in

x_{m}

, Text (T), Motion Mask (MM), Video (V), and partial video (pV). Classifier-free guidance (CFG). Steps (Vid/

s

) reports inference steps and video throughput. Task denotes reconstruction (Rec) and generation (Gen) .

pmf

is the proportion of masked frames and

h

is the exponent in adaptive weight

w

\mu

denotes the mean score; (RS) denotes rejection sampling (three samples per conditioning, best retained). Bold and Blue indicate best Rec and Gen performance.

\dagger

Inference steps not reported (DDPM default 1000).

{\ddagger}

Clip length unspecified (likely FVD 16 ).

Figure 2: Qualitative Results. Columns 1-5 show frames sampled between and including ED and ES, while column 6 presents the M-mode slice of the middle row over time.

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Medical Image Synthesis Cardiac MRI 4D Reconstruction Physics-Informed Generative Modeling Flow-Based Generative Modeling

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Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electrocardiogram (ECG) gating and repeated breath holds. Visual realism alone does not establish accurate patient-specific ejection fraction (EF) or ventricular volumes. We present PhaseFlow3D, a generative framework that synthesizes a complete 4D cine sequence from a single end-diastolic (ED) three-dimensional (3D) volume without ECG. To capture asymmetric systolic and diastolic dynamics, it represents the cardiac cycle as a piecewise linear phase anchored at ED and end-systolic (ES) time points. At inference, a population-level canonical template supplies this phase without patient-specific temporal information. A phase-conditioned rectified flow model generates a cardiac motion trajectory in latent space. Radial Contraction Decomposition converts each latent state into a 3D displacement field, combining a physics-informed radial component for centripetal myocardial contraction with an image-conditioned residual for rotation and out-of-plane motion. Each frame is generated by directly warping the ED volume, bypassing variational autoencoder decoding. On the combined ACDC and M&Ms benchmark, PhaseFlow3D achieves the lowest EF mean absolute error, the only positive left-ventricular volume-curve

R^2

, and the best distributional quality among compared methods. Ablations confirm each component's contribution. Downstream evaluations demonstrate the utility of the synthesized sequences and displacement fields for segmentation, pathology classification, label propagation, and myocardial strain analysis.

Abstract

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