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Modelling C-arm fluoroscopy and operating table kinematics via machine learning.

Faria Jaheen1, Vinod Gutta1, Pascal Fallavollita1,2

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.

Frontiers in Robotics and AI
|February 23, 2026
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Summary
This summary is machine-generated.

This study introduces a machine learning framework for efficient C-arm fluoroscopy modeling and workspace optimization. It improves surgical precision and safety by reducing collision risks during operations.

Keywords:
C-arm fluoroscopyforward kinematicsinverse kinematicsmachine learningoperating tablesurgery

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Area of Science:

  • Robotics and Machine Learning in Medical Devices
  • Surgical Navigation Systems
  • Medical Imaging Technology

Background:

  • Modular C-arm fluoroscopy systems are crucial for intraoperative imaging but face challenges in kinematic modeling and workspace optimization.
  • Ensuring collision-free movement and maximizing imaging access in multi-degree-of-freedom systems is complex.
  • Current methods often lack the scalability, robustness, and real-time performance needed for dynamic surgical environments.

Purpose of the Study:

  • To develop a data-efficient machine learning framework for kinematic modeling and workspace optimization in modular C-arm fluoroscopy systems.
  • To enable precise trajectory planning and collision avoidance in systems with 5 to 9 degrees of freedom.
  • To enhance intraoperative imaging access and reduce risks associated with system collisions.

Main Methods:

  • Utilized a comprehensive dataset of joint configurations and end-effector poses with annotated collision status.
  • Trained predictive models using expansive simulation-derived datasets and validated with simulated X-ray generation.
  • Implemented a machine learning framework for real-time inference and data-driven trajectory planning.

Main Results:

  • Achieved sub-millimetric positional accuracy and sub-degree angular precision in kinematic modeling.
  • Demonstrated real-time inference capabilities surpassing conventional methods in scalability, robustness, and computational latency.
  • Validated the framework's effectiveness in improving imaging access and reducing intraoperative collision risks.

Conclusions:

  • The proposed machine learning framework offers a data-driven solution for kinematic modeling and workspace optimization in complex C-arm systems.
  • This approach significantly enhances the precision and safety of robotic-assisted surgery by enabling efficient trajectory planning.
  • The framework provides a clinically relevant advancement for improving surgical workflows and patient outcomes.