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Motion Planning for Steerable Medical Needles

Ron Alterovitz1,3 and Ken Goldberg1,2

1Electrical Engineering and Computer Sciences, University of California, Berkeley

2Industrial Engineering and Operations Research, University of California, Berkeley

3UCSF Comprehensive Cancer Center, University of California, San Francisco

Abstract

We present motion planning algorithms for steerable medical needles, a new class of flexible, bevel-tip needles capable of following curved paths through soft tissue. Due to their greater mobility, steerable needles can reach targets inaccessible to traditional stiff needles. Planning motions for these needles to a target while avoiding obstacles is difficult due to nonholonomic constraints, the effects of tissue deformations during needle insertion, and uncertainty in needle motion due to complex needle/tissue interactions.

In this talk, we first consider the motion of steerable needles on an imaging plane. We present a planning algorithm that compensates for needle placement errors due to tissue deformations by using a physically-based simulation of needle insertion in soft tissue. Next, we present a motion planner that explicitly considers uncertainty in needle motion and maximizes the probability that the needle tip will successfully reach the target. The method is based on the Stochastic Motion Roadmap, a new sampling-based algorithmic framework for robot motion planning under uncertainty based on Markov Decision Processes and Dynamic Programming. Finally, we look at extensions towards 3D needle motion. We increase the control freedom to allow arbitrary rotations around the needle shaft and search for optimal needle trajectories between two positions and orientations in 3D space. By discretizing the 1D control space instead of the full 6D configuration space of the needle, we can find locally optimal needle paths in 3D environments with obstacles in less than a second.

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.
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.


Related Publications

  • Vincent Duindam, Ron Alterovitz, Shankar Sastry, and Ken Goldberg, "Screw-Based Motion Planning for Bevel-Tip Flexible Needles in 3D Environments with Obstacles," to appear in IEEE International Conference on Robotics and Automation (ICRA), May 2008.
  • Ron Alterovitz, Thierry Siméon, and Ken Goldberg, "The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty," in Proc. Robotics: Science and Systems, Jun. 2007.
  • 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


Links

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This page was last modified 00:30, April 2, 2008.