Abstract
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily rely on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPulse, an ECG-conditioned ECHO video generation model. ECHOPulse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPulse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures. Additionally, ECHOPulse can be easily generalized to other modality generation tasks, such as cardiac MRI, fMRI, and 3D CT generation. Codes and examples can be seen from https://github.com/levyisthebest/ECHOPulse_Prelease.
Recommended Citation
Y. Li and S. Kim and Z. Wu and H. Jiang and Y. Pan and P. Jin and S. Song and Y. Shi and X. Yu and T. Yang and T. Liu and Q. Li and X. Li, "Echopulse: ECG Controlled Echocardiograms Video Generation," 13th International Conference on Learning Representations Iclr 2025, pp. 43947 - 43967, arXiv, Jan 2025.
The definitive version is available at https://doi.org/10.48550/arXiv.2410.03143
Department(s)
Computer Science
Publication Status
Open Access
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 arXiv, All rights reserved.
Publication Date
01 Jan 2025
