Determining Safe Start Regions for Automated Steerable Needle Procedures

Core Concepts

A method to efficiently determine a safe start region around the nominal start pose of a steerable needle motion plan, from which the target can be reached while avoiding obstacles. The method considers the tradeoff between robustness to deviations in position versus orientation.

Abstract

The content describes a method to evaluate the robustness of a steerable needle motion plan's start pose to deviations in both position and orientation. The key highlights are:

Steerable needles enable novel minimally invasive medical procedures by following curved paths to avoid obstacles, but their deployment requires a handoff from a physician to an autonomous robot. Even small deviations from the planned start pose can make the target unreachable.

The authors introduce a metric that evaluates the robustness to such start pose deviations, considering the tradeoff between position and orientation tolerance.

The method uses Dubins paths to efficiently propagate orientation ranges backward along an existing motion plan, determining the maximum allowable deviations in both position and orientation from which the target remains reachable.

The intersection of the computed safe start region and the physician's reachable start region is identified as the set of safe start poses.

The method is demonstrated in simulation for an abstract scenario, a lung biopsy, and a liver biopsy, showing it can be combined with different motion planners and efficiently finds large safe start regions.

Stats

The steerable needle has a minimum radius of curvature rmin and a maximum length lneedle.
The needle's diameter is dneedle.
The total curvature of the needle's path cannot exceed π/2.

Quotes

"Even a small deviation from a planned start pose in either position or orientation could result in the target being unreachable."
"There is a tradeoff between maximizing robustness in position and in orientation, and our method allows the user to choose this tradeoff according to their preferences and the specific insertion scenario."

Key Insights Distilled From

Safe Start Regions for Medical Steerable Needle Automation

by Janine Hoels... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08558.pdf

Deeper Inquiries

How could this method be extended to handle uncertainty in the patient anatomy or needle dynamics during deployment

To handle uncertainty in patient anatomy or needle dynamics during deployment, the method could be extended by incorporating probabilistic models or Monte Carlo simulations. By introducing variability in the patient's anatomy or the needle's behavior, the algorithm could generate multiple possible scenarios and evaluate the robustness of the start poses in each case. This would provide a more comprehensive understanding of the potential challenges and allow for the selection of start poses that are robust across a range of uncertainties. Additionally, machine learning techniques could be employed to learn from past procedures and adapt the start pose selection based on real-time feedback during deployment.

What are potential limitations of the Dubins path-based approach, and how could alternative techniques be incorporated

One potential limitation of the Dubins path-based approach is its restriction to 2D spaces and constant curvature arcs, which may not fully capture the complexities of 3D needle deployment. Alternative techniques, such as sampling-based planners like Rapidly Exploring Random Trees (RRT), could be incorporated to handle the higher-dimensional spaces and non-constant curvature constraints of steerable needles. Additionally, optimization-based methods could be used to find more efficient paths that consider multiple objectives simultaneously, such as minimizing path length, maximizing obstacle clearance, and ensuring target reachability. By combining different planning approaches, the limitations of the Dubins path-based method can be mitigated, allowing for more robust and versatile motion planning for steerable needles.

How could this start pose robustness metric be integrated with other planning objectives, such as path length or obstacle clearance, to enable comprehensive optimization of steerable needle procedures

The start pose robustness metric could be integrated with other planning objectives by defining a multi-objective optimization problem. Each planning objective, such as path length, obstacle clearance, and start pose robustness, could be assigned a weight or priority level based on the specific requirements of the procedure. A multi-objective optimization algorithm, such as a weighted sum method or a Pareto optimization approach, could then be used to find a set of optimal solutions that balance all objectives. By considering the trade-offs between different planning objectives, the algorithm could generate motion plans that are not only collision-free and efficient but also robust to deviations in start poses, leading to safer and more reliable steerable needle procedures.