Automatic Force-Based Probe Positioning for Precise Robotic Ultrasound Acquisition

Jiang, Zhongliang; Grimm, Matthias; Zhou, Mingchuan; Hu, Ying; Esteban, Javier; Navab, Nassir

doi:10.1109/tie.2020.3036215

Cited by 59 publications

(30 citation statements)

References 29 publications

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“…Instead, a reinforcement learning framework is adopted to optimize the confidence map of ultrasound images, by adapting the movement of the ultrasound probe. All of the above-mentioned work only take the pose and the position of the probe as input, while in this paper the contact force between the probe and humans is also encoded, which is considered as a crucial factor during ultrasound scanning process [44].…”

Section: B Learning Of the Ultrasound Scanning Skillsmentioning

confidence: 99%

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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Section: B Learning Of the Ultrasound Scanning Skillsmentioning

confidence: 99%

Learning Ultrasound Scanning Skills from Human Demonstrations

Deng¹,

Lei²,

Wang³

et al. 2021

Preprint

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show abstract

“…As shown by Jiang et al, a combination of US images and force feedback from a 7-DOF robot can be used to control the alignment of the US probe in reference to the scanning surface [14]. Furthermore, Jiang et al demonstrated the importance of the US probe-to-surface alignment when acquiring images of human tissue [15].…”

Section: Introductionmentioning

confidence: 99%

RobUSt–An Autonomous Robotic Ultrasound System for Medical Imaging

et al. 2021

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Medical ultrasound (US) systems are widely used for the diagnosis of internal tissues. However, there are challenges associated with acquiring and interpreting US images, such as incorrect US probe placement and limited available spatial information. In this study, we expand the capabilities of medical US imaging using a robotic framework with a high level of autonomy. A 3D camera is used to capture the surface of an anthropomorphic phantom as a point cloud, which is then used for path planning and navigation of the US probe. Robotic positioning of the probe is realised using an impedance controller, which maintains stable contact with the surface during US scanning and compensates for uneven and moving surfaces. Robotic US positioning accuracy is measured to be 1.19±0.76mm. The mean force along US probe z-direction is measured to be 6.11±1.18N on static surfaces and 6.63±2.18N on moving surfaces. Overall lowest measured force of 1.58N demonstrates constant probe-to-surface contact during scanning. Acquired US images are used for the 3D reconstruction and multi-modal visualization of the surface and the inner anatomical structures of the phantom. Finally, K-means clustering is used to segment different tissues. Best segmentation accuracy of the jugular vein according to Jaccard similarity coefficient is measured to be 0.89. With such an accuracy, this system could substantially improve autonomous US acquisition and enhance the diagnostic confidence of clinicians.

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“…Recently, since robotic manipulators are much more accurate and stable than human operators, robotic US systems (RUSS) have become a potential solution toward automatic US scans [8]- [11]. To accurately control the probe orientation, Jiang et al developed a method to align the probe centerline with the normal direction of a tested object using force data [12]. Besides, Jiang et al proposed a motion-aware 3D RUSS to combine the flexibility of the free-hand US and the accuracy and stability of robot [13].…”

Section: Introductionmentioning

confidence: 99%

Deformation-Aware Robotic 3D Ultrasound

Jiang,

Zhou,

et al. 2021

Preprint

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Tissue deformation in ultrasound (US) imaging leads to geometrical errors when measuring tissues due to the pressure exerted by probes. Such deformation has an even larger effect on 3D US volumes as the correct compounding is limited by the inconsistent location and geometry. This work proposes a patientspecified stiffness-based method to correct the tissue deformations in robotic 3D US acquisitions. To obtain the patient-specified model, robotic palpation is performed at sampling positions on the tissue. The contact force, US images and the probe poses of the palpation procedure are recorded. The contact force and the probe poses are used to estimate the nonlinear tissue stiffness. The images are fed to an optical flow algorithm to compute the pixel displacement. Then the pixel-wise tissue deformation under different forces is characterized by a coupled quadratic regression. To correct the deformation at unseen positions on the trajectory for building 3D volumes, an interpolation is performed based on the stiffness values computed at the sampling positions. With the stiffness and recorded force, the tissue displacement could be corrected. The method was validated on two blood vessel phantoms with different stiffness. The results demonstrate that the method can effectively correct the force-induced deformation and finally generate 3D tissue geometries.

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Automatic Force-Based Probe Positioning for Precise Robotic Ultrasound Acquisition

Cited by 59 publications

References 29 publications

Learning Ultrasound Scanning Skills from Human Demonstrations

Learning Ultrasound Scanning Skills from Human Demonstrations

RobUSt–An Autonomous Robotic Ultrasound System for Medical Imaging

Deformation-Aware Robotic 3D Ultrasound

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