ECHOPulse: ECG controlled echocardio-grams video generation
Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin,, Sifan Song, Yucheng Shi, Tianming Liu, Quanzheng Li, Xiang Li

TL;DR
ECHOPulse is a novel ECG-conditioned model that rapidly generates high-quality echocardiogram videos using tokenization and time-series prompts, enhancing automated cardiac assessment and disease monitoring.
Contribution
It introduces the first ECG-conditioned ECHO video generation model that accelerates synthesis and eliminates complex prompts, enabling controllable and efficient synthetic data creation.
Findings
Achieves state-of-the-art quality in ECHO video generation
Demonstrates fast inference with tokenization techniques
Generalizes to other medical imaging modalities
Abstract
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies 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…
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Taxonomy
TopicsECG Monitoring and Analysis
MethodsVQ-VAE
