Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

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Publication Details

Journal
IEEE Transactions on Medical Robotics and Bionics
Published
2025-03-12
DOI
https://doi.org/10.1109/tmrb.2025.3550663
Citations
1
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.52

Funders

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article

Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

Long Lei, Pheng‐Ann Heng, Yu Zhang, Zheng Li et al.
1 citations
IEEE Transactions on Medical Robotics and Bionics
Lung Cancer Diagnosis and Treatment
1.52
article

Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

Long Lei, Pheng‐Ann Heng, Yu Zhang, Zheng Li, Qiong Wang, Xiao Luo, Shujun Wang, Juzheng Miao, Zixing Jiang, Yingbai Hu
article en
1 citations

Abstract

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

IEEE Transactions on Medical Robotics and BionicsVol. 7(2)
Hong Kong Polytechnic University (HK), Chinese University of Hong Kong (HK), Chinese Academy of Sciences (CN), Shenzhen Institutes of Advanced Technology (CN)
Innovation and Technology Commission - Hong Kong
Openalex Percentile: Top 21%
Lung Cancer Diagnosis and Treatment
1.52
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