Study of a 6DOF robot assisted ultrasound scanning system and its simulated control handle

Guan, Xiaolong; Wu, Haotian; Hou, Xilong; Teng, Quincy; Wei, Shiyou; Jiang, Tao; Zhang, Jiantao; Wang, Ban; Yang, Jia; Xiong, Linfei

doi:10.1109/iccis.2017.8274821

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…According to the extent of the system autonomy, robotic ultrasound can be categorized into three levelsteleoperated, semi-autonomous, and full-autonomous. A teleoperated robotic ultrasound system usually contains two main parts-teacher site and student site [18][19][20]. The motion of the student robot is completely determined by the teacher, usually a trained sonographer, through different kinds of interaction devices, including a 3D space mouse [18], inertial measurement unit (IMU) handle [20,21], and haptic interface [21].…”

Section: Introductionmentioning

confidence: 99%

“…A teleoperated robotic ultrasound system usually contains two main parts-teacher site and student site [18][19][20]. The motion of the student robot is completely determined by the teacher, usually a trained sonographer, through different kinds of interaction devices, including a 3D space mouse [18], inertial measurement unit (IMU) handle [20,21], and haptic interface [21]. While for a semi-autonomous robotic ultrasound system, the motion of the student robot is only partly determined by the teacher [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Learning Robotic Ultrasound Skills from Human Demonstrations

Li¹,

Deng²

2022

Cognitive Robotics and Adaptive Behaviors

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Robotic ultrasound system plays a vital role in assisting or even replacing sonographers in some cases. However, modeling and learning ultrasound skills from professional sonographers are still challenging tasks that hinder the development of ultrasound systems’ autonomy. To solve these problems, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations1. First, ultrasound scanning skills are encapsulated into a high-dimensional multi-modal model, which takes ultrasound images, probe pose, and contact force into account. The model’s parameters can be learned from clinical ultrasound data demonstrated by professional sonographers. Second, the target function of autonomous ultrasound examinations is proposed, which can be solved roughly by the sampling-based strategy. The sonographers’ ultrasound skills can be represented by approximating the limit of the target function. Finally, the robustness of the proposed framework is validated with the experiments on ground-true data from sonographers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Robotic Ultrasound Skills from Human Demonstrations

Li¹,

Deng²

2022

Cognitive Robotics and Adaptive Behaviors

View full text Add to dashboard Cite

show abstract

“…According to the level of the system autonomy, robotic ultrasound system can be categorized into three levels: tele-operated, semi-autonomous and fullautonomous. A tele-operated robotic ultrasound system usually contains two main parts: teacher site and student site [18]- [20]. The motion of the student robot is completely determined by the teacher, usually a trained sonographer through different kinds of interaction devices including 3D space mouse [18],…”

Section: Introductionmentioning

confidence: 99%

“…inertial measurement unit (IMU) handle [20], [21], haptic interface [21], etc. While for a semi-autonomous robotic ultrasound system, the motion of the student robot is partly determined by the teacher [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

“…Paper Autonomy Degree Specific Target Modality Guidance Publication Year [18] tele-operated no force, orientation, position human 2015 [19] tele-operated no force, orientation, position human 2016 [20] tele-operated no force, orientation, position human 2017 [21] tele-operated no force, orientation, position human 2020 [22] semi-autonomous no force, orientation, position, elastogram elastogram, human 2017 [23] semi-autonomous no force, orientation, position, vision CNN, human 2019 [24] semi-autonomous yes force, orientation, position trajectory, human 2019 [30] semi-autonomous yes force, orientation, position, image CNN, human 2020 [25] full-autonomous yes force, orientation, position, vision, image, MRI vision, MRI, confidence map 2016 [26] full-autonomous yes force, orientation, position, image SVM 2017 [27] full-autonomous no force, orientation, position, vision vision 2018 [28] full-autonomous yes force, orientation, position, vision, MRI vision, MRI 2016 [29] full-autonomous yes force, position, vision RL 2021 learned model is used to evaluate the current quality of the ultrasound image. In order to obtain a high quality ultrasound image, a sampling-based approach is used to adjust the probe motion.…”

Section: Introductionmentioning

confidence: 99%

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Learning Ultrasound Scanning Skills from Human Demonstrations

Deng¹,

Lei²,

Wang³

et al. 2021

Preprint

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Recently, the robotic ultrasound system has become an emerging topic owing to the widespread use of medical ultrasound. However, it is still a challenging task to model and to transfer the ultrasound skill from an ultrasound physician. In this paper, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations. First, the ultrasound scanning skills are encapsulated into a highdimensional multi-modal model in terms of interactions among ultrasound images, the probe pose and the contact force. The parameters of the model are learned using the data collected from skilled sonographers' demonstrations. Second, a samplingbased strategy is proposed with the learned model to adjust the extracorporeal ultrasound scanning process to guide a newbie sonographer or a robot arm. Finally, the robustness of the proposed framework is validated with the experiments on real data from sonographers.

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Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends

et al. 2021

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Purpose of Review This review provides an overview of the most recent robotic ultrasound systems that have contemporary emerged over the past five years, highlighting their status and future directions. The systems are categorized based on their level of robot autonomy (LORA). Recent Findings Teleoperating systems show the highest level of technical maturity. Collaborative assisting and autonomous systems are still in the research phase, with a focus on ultrasound image processing and force adaptation strategies. However, missing key factors are clinical studies and appropriate safety strategies. Future research will likely focus on artificial intelligence and virtual/augmented reality to improve image understanding and ergonomics. Summary A review on robotic ultrasound systems is presented in which first technical specifications are outlined. Hereafter, the literature of the past five years is subdivided into teleoperation, collaborative assistance, or autonomous systems based on LORA. Finally, future trends for robotic ultrasound systems are reviewed with a focus on artificial intelligence and virtual/augmented reality.

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Study of a 6DOF robot assisted ultrasound scanning system and its simulated control handle

Cited by 12 publications

References 12 publications

Learning Robotic Ultrasound Skills from Human Demonstrations

Learning Robotic Ultrasound Skills from Human Demonstrations

Learning Ultrasound Scanning Skills from Human Demonstrations

Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends

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