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Particle Filter Based Active Localization of Target and Needle in Robotic Image-Guided Intervention Systems.

Mark Renfrew¹, Zhuofu Bai¹, M Cenk Cavuşoğlu¹

¹Dept. of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106.

IEEE International Conference on Automation Science and Engineering (CASE) : [Proceedings]. IEEE Conference on Automation Science and Engineering

|November 11, 2014

Summary

This summary is machine-generated.

This study introduces a new probabilistic method for robotic-assisted surgery, enhancing the accuracy of locating needles and targets during image-guided interventions using particle filters.

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Area of Science:

Robotics and Medical Imaging
Computer-Aided Surgery
Probabilistic Methods

Background:

Accurate localization of surgical instruments and targets is crucial for effective image-guided interventions.
Current methods may face challenges in real-time precision during robotic procedures.

Purpose of the Study:

To develop and validate a probabilistic active localization method for needles and targets in robotic image-guided interventions.
To enhance the precision and reliability of intra-operative localization using medical imaging.

Main Methods:

A probabilistic approach framing active localization as an information maximization problem.
Utilizing particle filters for representing and estimating needle and target states.
Employing intra-operative medical imaging, such as CT and ultrasound, for active localization.

Main Results:

The proposed method demonstrated effective active localization of needle and target positions.
Particle filters successfully represented and updated beliefs about the states.
Simulation studies validated the performance and accuracy of the approach.

Conclusions:

The developed probabilistic method offers a robust solution for active localization in robotic image-guided interventions.
This approach has the potential to improve surgical accuracy and patient outcomes.
Further validation in clinical settings is warranted.