Motion Planning for Steerable Needles in Soft Tissue

Ron Alterovitz1, Allison Okamura2, Gregory S. Chirikjian2, Michael Branicky3, Andrew Lim4, and Ken Goldberg4,5

1 Department of Computer Science, University of North Carolina at Chapel Hill (starting January 2009)
2 Department of Mechanical Engineering, The Johns Hopkins University
3 Department of Electrical Engineering and Computer Science, Case Western Reserve University
4 Department of Industrial Engineering and Operations Research, University of California, Berkeley
5 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley

Needle insertion is a critical step in many diagnostic and therapeutic medical procedures, including biopsy to obtain a specific tissue sample for testing, drug injections for anesthesia, or radioactive seed implantation for brachytherapy cancer treatment. We explore motion planning for a new class of flexible, bevel-tip medical needles that are capable of following curved paths through soft tissue. These needles can be steered to previously unreachable targets in soft tissue by controlling the needle's bevel direction. Motion planning for needle steering is complicated by nonholonomic constraints on the needle's motion, soft tissue deformation during needle insertion, and uncertainty in tissue properties and needle/tissue interaction forces.

We develop two motion planning algorithms to steer a needle around obstacles to a target in a planar slice of soft tissue. The first planner, which assumes stiff tissue relative to the needle, efficiently samples the state space, formulates the planning problem as a Markov Decision Process (MDP) to handle needle motion uncertainty, and solves for a solution using dynamic programming to maximize the probability of successfully reaching the target. Results demonstrate that explicitly accounting for uncertainty can lead to significantly different motion plans compared to traditional shortest paths, which may pass through narrow gaps between obstacles and are more likely to fail in the presence of uncertainty. The second planner combines a finite element model of soft tissue with numerical optimization to compensate for predicted tissue deformations caused by needle insertion. We apply the planners to steerable needles and generate plans for targets that are unreachable by rigid needles.


(a) Shortest path solution

(b) Maximizing probability of success

Fig. 1. The goal is to insert the needle from the green region on the left to reach a target (green circle) without touching critical areas (indicated by polygonal obstacles). The prostate is outlined in orange in the ultrasound imaging plane. The motion planner computes a sequence of insertions and direction changes (indicated by dots) to steer the needle to the target. Shortest paths (a) may pass close to obstacles, resulting in failure when the needle's motion is deflected. Explicitly considering motion uncertainty at the planning stage (b) can increase the probability of success by more clearly avoiding obstacles in the presence of uncertainty.


(a) Human Prostate, Tumor Target, and Obstacles

(b) Bevel-left Needle Trajectory Plan

(c) Bevel-right Needle Trajectory Plan

Fig. 2. In this example based on an MR image of the prostate, a biopsy needle attached to a rigid rectal probe (black half-circle) is inserted into the prostate (outlined in yellow) using simulation. Obstacles (red polygons) and the target (green cross) are overlaid on the image. The target is not accessible from the rigid probe by a straight line path without intersecting obstacles. However, bevel-tip needles bend as they are inserted into soft tissue. Our planner computes a locally optimal bevel-left needle insertion plan that reaches the target, avoids obstacles, and minimizes insertion distance (b). Using different initial conditions, our planner generates a plan for a bevel-right needle (c). Due to tissue deformation, the needle paths do not have constant curvature.

Publications/Presentations

  1. Ron Alterovitz, Michael Branicky, and Ken Goldberg, "Constant-Curvature Motion Planning Under Uncertainty with Applications in Image-Guided Medical Needle Steering," in Proc. Workshop on the Algorithmic Foundations of Robotics, Jul. 2006. (Download PDF)
  2. Ron Alterovitz, Andrew Lim, Ken Goldberg, Gregory S. Chirikjian, and Allison M. Okamura, "Steering Flexible Needles Under Markov Motion Uncertainty," in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Aug. 2005, pp. 120-125. (Download PDF)
  3. Ron Alterovitz, Ken Goldberg, and Allison Okamura, "Planning for Steerable Bevel-tip Needle Insertion Through 2D Soft Tissue with Obstacles," in Proc. IEEE International Conference on Robotics and Automation (ICRA), Apr. 2005, pp. 1652-1657. (Download PDF)
  4. Robert J. Webster III, Allison M. Okamura, Noah J. Cowan, Gregory S. Chirikjian, Ken Goldberg, and Ron Alterovitz, "Distal bevel-tip needle control device and algorithm,'' US patent pending 11/436,995, May 2006.

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