Authors: Yang Yue*, Yulin Wang*, Haojun Jiang, Pan Liu, Shiji Song, Gao Huang#.
*: Equal contribution, #: Corresponding author.
Echocardiography probe guidance helps sonographers capture standard heart views, but requires significant skill and experience. AI-assisted systems aim to automate this process by interpreting ultrasound images and predicting precise probe movements, making cardiac care more accessible and consistent.
EchoWorld is a motion-aware world modeling framework that learns both heart anatomy and the visual effects of probe motion. EchoWorld employs a pre-training strategy inspired by world modeling principles, where the model predicts masked anatomical regions and simulates the visual outcomes of probe adjustments. Built upon this pre-trained model, we introduce a motion-aware attention mechanism in the fine-tuning stage that effectively integrates historical visual-motion data, enabling precise and adaptive probe guidance. Trained on more than one million ultrasound images from over 200 routine scans, our model outperforms existing methods in probe guidance accuracy.
This code is developed on the top of MAE and I-JEPA
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Due to data privacy and institutional restrictions, the dataset used in this study cannot be publicly released.