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A mechanics-based model for a tendon-driven active needle navigating inside a multiple-layer tissue.

Blayton Padasdao¹, Bardia Konh²

¹University of Hawaii at Manoa, Honolulu, HI, 96822, USA.

Journal of Robotic Surgery

|March 30, 2024

Summary

This summary is machine-generated.

A new model accurately predicts the deflection of a tendon-driven active needle navigating through multi-layer soft tissues. This advancement enhances precision for minimally invasive procedures like brachytherapy and biopsy.

Keywords:

Active tendon-driven needle Mechanics-based model Real-time tracking Robotic control Ultrasound tracking

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Area of Science:

Medical Robotics and Devices
Biomedical Engineering
Surgical Navigation

Background:

Percutaneous procedures such as brachytherapy and biopsy demand flexible active needles for precise tissue navigation and accurate target placement.
Previous research introduced a tendon-driven active needle for tissue navigation, necessitating further development for complex tissue environments.

Purpose of the Study:

To develop and validate a novel predictive model for the deflection of a tendon-driven active needle.
To assess the model's accuracy in steering through multiple-layer soft tissues with varying mechanical properties.

Main Methods:

A multi-layer phantom tissue with localized stiffness variations was created and characterized using indentation tests.
A robotic system was employed to insert and actively bend the tendon-driven needle within the phantom tissue.
Ultrasound imaging was utilized for real-time needle tracking during insertion and bending experiments for model validation.

Main Results:

The developed model demonstrated high accuracy in predicting needle deflection within the multi-layer soft tissue phantom.
Comparison between simulation results from the proposed model and empirical data confirmed its predictive capabilities.
The model effectively captured needle behavior in heterogeneous tissue environments with differing stiffness.

Conclusions:

The validated model provides accurate predictions of tendon-driven active needle deflection in multi-layer soft tissues.
This predictive capability is crucial for enhancing the precision and success rates of image-guided minimally invasive procedures.
The developed model represents a significant step towards improved robotic needle steering in complex anatomical regions.