Bilinear Model Predictive Control Framework of the OncoReach, a Tendon-Driven Steerable Stylet for Brachytherapy

2026-04-06

AI Summary

This work addresses the limited compatibility of existing modeling and control approaches with standard clinical brachytherapy needles, which hinders the clinical translation of steerable stylets. To overcome this, the study introduces bilinear model predictive control (BiMPC) for the first time to a tendon-driven steerable stylet system compatible with commercial treatment needles. The method maps three virtual inputs—insertion velocity and two bending rates—to physical inputs (insertion velocity and tendon tensions) to achieve high-precision trajectory tracking. Integrating geometric bilinear modeling, image-guided tip tracking, and experiments in tissue phantoms, the system demonstrates sub-2 mm tracking error (3% of insertion length) in open-loop operation and achieves a closed-loop fixed-target tracking error as low as 1.45 mm (1.7% of insertion length). Successful tracking of moving targets further confirms its clinical feasibility.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Steerable needles have the potential to improve interstitial brachytherapy by enabling curved trajectories that avoid sensitive anatomical structures. However, existing modeling and control approaches are primarily developed for custom needle designs and are not directly applicable to stylets compatible with commercially available clinical needles. This paper presents a bilinear model predictive control (MPC) framework for a tendon-driven steerable stylet integrated with a standard brachytherapy needle. \textcolor{black}{A geometric bilinear model is formulated with three virtual inputs (an insertion speed and two bending rates) which are mapped to physically realizable inputs consisting of the insertion speed and the associated tendon tensions.} The approach is validated through simulations and physical insertion experiments in tissue-mimicking phantom material using image-based tip tracking. While open-loop model validation yielded estimation errors below $2$~mm, corresponding to $3\%$ of the inserted needle length, and closed-loop fixed-target tracking achieved an error as low as $1.45$~mm, corresponding to $1.7\%$ of the inserted length, experiments showed larger position errors in certain bending directions, reaching $8.3$~mm, or $7.8\%$ of the inserted length. Overall, the results demonstrate the feasibility of fixed-target positioning and moving-target trajectory tracking for clinically compatible steerable brachytherapy systems, while highlighting necessary areas for future improvements in calibration and sensing.

Problem

• steerable needles
• brachytherapy
• model predictive control
• tendon-driven
• clinically compatible

Innovation

• bilinear model predictive control
• steerable stylet
• tendon-driven actuation
• brachytherapy needle
• trajectory tracking