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Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography Via 2D LiDAR-Based 3D Reconstruction

Mariadas Capsran Roshan, Edgar Mauricio Hidalgo Florez, Mats Isaksson, Michelle Dunn, Jagannatha Charjee Pyaraka

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Key figure (auto-extracted from paper)
A robot-mounted 2D LiDAR can accurately reconstruct a patient's chest surface and automatically estimate an initial ultrasound probe pose with sub-centimeter precision, bypassing the limitations of vision-based methods.
Teleoperated echocardiography 2D LiDAR Probe pose estimation 3D surface reconstruction Robotic ultrasound Extrinsic calibration

Problem

Teleoperated robotic echocardiography reduces diagnostic delays but currently requires lengthy, expert-dependent manual probe placement. Existing vision-based automation methods fail in clinical settings due to sensitivity to lighting, texture, and anatomical variability.

Approach

The system uses a robot-mounted 2D LiDAR to perform two linear sweeps across the chest, reconstructing the surface in 3D. Scale-augmented rigid registration then aligns this reconstruction with anatomical templates to automatically compute the optimal initial probe position and orientation.

Key results

  • First 3D human surface reconstruction using robot-mounted 2D LiDAR
  • Extrinsic calibration achieving 1.82 mm RMS residual and <0.2° rotational uncertainty
  • Mannequin validation yielding 2.78±0.21 mm mean surface reconstruction error
  • Human trials achieving 20–30 mm placement accuracy with <4 mm inter-trial variation

Why it matters

Provides a robust, lighting-invariant, and privacy-preserving method to automate probe initialization, reducing operator workload and procedure time in teleoperated cardiac imaging.

Abstract

Echocardiography is a key imaging modality for cardiac assessment but remains highly operator-dependent, and access to trained sonographers is limited in underserved settings. Teleoperated robotic echocardiography has been pro- posed as a solution. However, clinical studies report longer ex- amination times than manual procedures, increasing diagnostic delays and operator workload. Automating non-expert tasks, such as automatically moving the probe to an ideal starting pose, offers a pathway to reduce this burden. Prior vision- and depth-based approaches to estimate an initial probe pose are sensitive to lighting, texture, and anatomical variability. We propose a robot-mounted 2D LiDAR-based approach that reconstructs the chest surface in 3D and estimates the initial probe pose automatically. To the best of our knowledge, this is the first demonstration of robot-mounted 2D LiDAR used for 3D reconstruction of a human body surface. Through plane-based extrinsic calibration, the transformation between the LiDAR and robot base frames was estimated with an overall root mean square (RMS) residual of 1.82 mm and rotational uncertainty below 0.2◦. The chest front surface, reconstructed from two linear LiDAR sweeps, was aligned with scale-augmented rigid registration to identify an initial probe pose. Mannequin-based study assessing reconstruction accuracy showed mean surface errors of 2.78±0.21 mm. Human trials (N=5) evaluating the proposed approach found probe initial points typically 20–30 mm from the clinically defined initial point, while the variation across repeated trials on the same subject was less than 4 mm.

Index terms

Medical Robots and Systems Computer Vision for Medical Robotics Robotics in Under-Resourced Settings

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