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

## TL;DR

This paper introduces a machine learning framework to improve the performance of C-arm fluoroscopy systems during surgeries.

## Contribution

A data-efficient machine learning framework for kinematic modeling and workspace optimization in C-arm fluoroscopy systems.

## Key findings

- The framework achieves sub-millimetric positional accuracy and sub-degree angular precision.
- It enables real-time inference with better scalability and robustness than conventional methods.
- The models reduce intraoperative collision risks and improve imaging access.

## Abstract

This work presents a machine learning driven framework for data-efficient kinematic modeling and workspace optimization in modular C-arm fluoroscopy systems integrated with operating tables. A comprehensive dataset of joint configurations and end-effector poses annotated with voxelized collision status enables the training of predictive models across multiple system configurations ranging from 5 to 9 degrees of freedom. Leveraging expansive simulation-derived datasets, as well as clinical assessment through simulated X-ray generation, the models are trained and validated, achieving sub-millimetric positional accuracy and sub-degree angular precision while delivering real-time inference that surpasses conventional methods in scalability, robustness, and computational latency. The proposed framework demonstrates the viability of data-driven trajectory planning in multi-degree of freedom C-arm systems, providing a clinically relevant solution for improving imaging access and reducing intraoperative collision risks.