Automatic probe guidance for volumetric liver ultrasound acquisition via imitation learning from a virtual expert.

Authors

Taiyu Han, Hanying Liang, Qi Zeng, Yan Kang, Hongen Liao, Guochen Ning, Septimiu E Salcudean

Published in

International journal of computer assisted radiology and surgery. Sep 06, 2026. Epub Sep 06, 2026.

Abstract

Volumetric liver ultrasound (US) plays an important role in clinical diagnosis but remains highly dependent on operator expertise, particularly during target view localization. This study aims to develop an automatic probe guidance framework that reduces reliance on manual demonstrations and additional sensing hardware, while supporting robust volumetric liver US acquisition.
We propose an image-based imitation learning framework that learns probe guidance policies from a virtual expert in a simulated US scanning environment. A simulation pipeline is constructed using cross-modal medical images and a hybrid US simulator that combines physics-based ray casting with generation-based image synthesis to produce anatomically consistent and acoustically realistic US images. Optimal scanning trajectories are generated based solely on target views typically available in clinical practice. To improve robustness, pose-level and image-level data augmentations are introduced, and US observations are encoded into an anatomy-aware state representation for intercostal liver scanning.
Experimental results in simulation and real clinical data demonstrate that the proposed framework achieves accurate and stable target view localization for volumetric liver US acquisition. Compared with baseline and ablated models, the method shows improved localization accuracy, increased liver coverage, and reduced rib interference, while maintaining robustness across different anatomical conditions.
This work presents a data-efficient and clinically practical solution for automatic probe guidance in volumetric liver US. By leveraging realistic simulation, virtual expert demonstrations, and anatomy-aware image representations, the proposed framework enables effective learning of probe movements without requiring manual trajectory annotations or additional sensors. The results suggest strong potential for integration into computer-assisted and robotic US systems.

PMID:
42701918
Bibliographic data and abstract were imported from PubMed on 07 Sep 2026.

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