Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model

Author:

Bentley Michael1ORCID,Rucker Caleb2,Reddy Chakravarthy3,Salzman Oren4,Kuntz Alan1

Affiliation:

1. Robotics Center and Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112, USA

2. The Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA

3. Huntsman Cancer Institute and School of Medicine, University of Utah, Salt Lake City, UT 84112, USA

4. Department of Computer Science, Technion–Israel Institute of Technology, Technion City, Haifa 3200003, Israel

Abstract

Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the potential to reduce the invasiveness of many medical procedures. However, inserting these needles with curved trajectories increases the risk of tissue damage due to perpendicular forces exerted on the surrounding tissue by the needle’s shaft, potentially resulting in lateral shearing through tissue. Such forces can cause significant tissue damage, negatively affecting patient outcomes. In this work, we derive a tissue and needle force model based on a Cosserat string formulation, which describes the normal forces and frictional forces along the shaft as a function of the planned needle path, friction model and parameters, and tip piercing force. We propose this new force model and associated cost function as a safer and more clinically relevant metric than those currently used in motion planning for steerable needles. We fit and validate our model through physical needle robot experiments in a gel phantom. We use this force model to define a bottleneck cost function for motion planning and evaluate it against the commonly used path-length cost function in hundreds of randomly generated three-dimensional (3D) environments. Plans generated with our force-based cost show a 62% reduction in the peak modeled tissue force with only a 0.07% increase in length on average compared to using the path-length cost in planning. Additionally, we demonstrate planning with our force-based cost function in a lung tumor biopsy scenario from a segmented computed tomography (CT) scan. By directly minimizing the modeled needle-to-tissue force, our method may reduce patient risk and improve medical outcomes from steerable needle interventions.

Funder

Division of Information and Intelligent Systems

Ministry of Science and Technology, Israel

United States-Israel Binational Science Foundation

Division of Civil, Mechanical and Manufacturing Innovation

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Human-Computer Interaction,Biomedical Engineering

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