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Physiological motion modeling for organ-mounted robots.

Nathan A Wood1, David Schwartzman2, Marco A Zenati3

  • 1The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.

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Summary
This summary is machine-generated.

This study presents a new framework for modeling periodic motion in organ-mounted robots, crucial for enhancing surgical navigation. The developed models accurately capture heart and respiratory movements, improving localization and dynamic mapping in real-time.

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Area of Science:

  • Robotics in Medicine
  • Surgical Navigation Systems
  • Biomedical Engineering

Background:

  • Organ-mounted robots are used to compensate for physiological motion during procedures.
  • This motion, often overlooked, contains valuable information for localization and dynamic mapping.

Purpose of the Study:

  • To develop and validate models for estimating periodic motion (position and orientation) of organs.
  • To explore the utility of this motion data for enhancing model-guided surgical procedures.

Main Methods:

  • Proposed models for estimating periodic motion in both position and orientation.
  • Tested models on animal data to identify optimal model orders.
  • Demonstrated methods for online parameter identification.

Main Results:

  • Models using exponential coordinates and Euler-angle parameterizations achieved accuracy comparable to quaternion representations with fewer parameters.
  • Incorporating more than four cardiac or three respiration harmonics did not improve accuracy.
  • Online methods achieved accuracy similar to offline methods within three respiration cycles.

Conclusions:

  • A comprehensive framework for accurately modeling periodic deformation on the heart surface in closed-chest procedures has been established.
  • These models can provide dynamic information to static preoperative maps, aiding surgical guidance.