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Measurement and Potential Field-Based Patient Modelling for Model-Mediated Tele-Ultrasound

Ryan S. Yeung, David G. Black, Septimiu E. Salcudean

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AI summary

Key figure (auto-extracted from paper)
Incorporating real-time force and torque measurements into a potential field model significantly improves haptic feedback accuracy for remote tele-ultrasound.
Tele-ultrasound Model-mediated teleoperation Haptic feedback Potential field modeling Remote medical imaging Force rendering

Problem

Teleoperated ultrasound lacks accurate force feedback due to communication delays and the inability of prior models to capture spatial variations in patient tissue stiffness.

Approach

The method converts a patient surface point cloud into a voxelized potential field and updates it using measured probe positions, forces, and torques via convex quadratic optimization to render accurate haptic cues.

Key results

  • Reduced force magnitude RMSE by an average of 7.42 N
  • Reduced force vector angle error by an average of 3.71°
  • Reduced torque vector angle error by an average of 64.0°
  • Achieved real-time capable force rendering at 2.74 ms per contact

Why it matters

Enables more transparent haptic feedback for remote sonographers, expanding access to high-quality diagnostic ultrasound in underserved communities.

Abstract

Teleoperated ultrasound can improve diagnostic medical imaging access for remote communities. Having ac- curate force feedback is important for enabling sonographers to apply the appropriate probe contact force to optimize ultrasound image quality. However, large time delays in com- munication make direct force feedback impractical. Prior work investigated using point cloud-based model-mediated teleoper- ation and internal potential field models to estimate contact forces and torques. We expand on this by introducing a method to update the internal potential field model of the patient with measured positions, forces and torques for more transparent model-mediated tele-ultrasound. We first generate a point cloud model of the patient’s surface and transmit this to the sonographer in a compact data structure. This is converted to a static voxelized volume where each voxel contains a potential field value. These values determine the forces and torques, which are rendered based on overlap between the voxelized volume and a point shell model of the ultrasound transducer. We solve for the potential field using a convex quadratic that combines the spatial Laplace operator with measured forces and torques. This was evaluated on volunteers (n = 4) by assessing the accuracy of rendered forces and torques. Results showed the addition of measurements to the model reduced the force magnitude RMSE by an average of 7.42 N, the force vector angle error by an average of 3.71◦, and the torque vector angle error by an average of 64.0◦compared to using only Laplace’s equation.

Index terms

Medical Robots and Systems Telerobotics and Teleoperation Haptics and Haptic Interfaces

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