VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance
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Abstract (arXiv)
Echocardiography is a critical tool for detecting heart diseases, yet its steep operational difficulty causes a shortage of skilled personnel. Probe guidance systems, which assist in acquiring high-quality images, offer a promising solution to lower this operational barrier. However, robust probe guidance remains challenging due to significant individual variability. This variability manifests as differences in low-level features within two-dimensional (2D) images, which complicates image feature understanding, and differences in individual three-dimensional (3D) structures, which poses challenges for precise navigation. To address these challenges, we first propose leveraging the robust image representations learned by ultrasound foundation models from vast datasets. Yet, applying these models to probe navigation is non-trivial due to their lack of understanding of individual 3D structures. To this end, we meticulously design a Vision-Action Adapter (VA-Adapter) to online inject the capability of understanding individual 3D structures. Specifically, by embedding the VA-Adapter into the foundation model's image encoder, the model can infer cardiac anatomy from historical vision-action sequences, mimicking the cognitive process of a sonographer. Extensive experiments on a dataset with over 1.31M samples demonstrate that the VA-Adapter outperforms strong probe guidance models while requiring approximately 33 times fewer trained parameters. Code is available at https://github.com/LeapLabTHU/VA-Adapter.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 8 Oct 2025.
Builds on
21 of 34 references.
References · 21
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9 more
- 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
- 2020Automatic Probe Movement Guidance for Freehand Obstetric Ultrasoundcited by 27
- 2025CheXWorld: Exploring Image World Modeling for Radiograph Representation Learningcited by 18
- 2024Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Modelcited by 14
- 2025EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidancecited by 13
- 2024Goal-conditioned reinforcement learning for ultrasound navigation guidancecited by 6
- 2024Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-traincited by 6
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- 2025UltraHiT: A Hierarchical Transformer Architecture for Generalizable Internal Carotid Artery Robotic Ultrasonographycited by 2
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