3D simulation of needle steering in the prostate for brachytherapy cancer treatment
Needle insertion is a critical step in many diagnostic and therapeutic
medical procedures, including biopsy, drug injection, and radioactive seed
implantation for brachytherapy cancer treatment.
We are developing steerable needles, a new class of flexible, bevel-tip medical needles that are capable of following curved paths through soft tissue. By controlling the needle's bevel direction via base rotations, these needles can be steered to previously unreachable targets in soft tissue.
Directing steerable needles to specific clinical targets while avoiding anatomical obstacles requires planning paths through the patient's anatomy. For steerable needles, this planning is often beyond the capabilities of human intuition due to the needles' complex kinematics and the effects of tissue deformation, tissue inhomogeneities, and other causes of motion uncertainty.
We are developing automatic planning algorithms and physically-based simulations to enable physicians to harness the full potential of steerable needles.
Video of our interactive 3D simulator of needle steering in the prostate for brachytherapy cancer treatment.
We are developing physically-based simulations and motion planning algorithms for steerable needles to assist physicians in guiding these devices around anatomical obstacles to clinical targets. One major challenge is tissue deformations - inserting needles into soft tissues causes the surrounding soft tissues to deform, and ignoring these deformations can result in substantial placement error. Computer simulations that model soft tissue deformations can assist in preoperative planning by enabling clinicians a priori to optimize paths for needle insertion procedures. We are developing 2D and 3D simulations of steerable needle procedures by modeling tissue deformations using a finite element method, modeling needle frictional and cutting forces, and using novel re-meshing to ensure conformity of the mesh to the curvilinear needle path.
We also developed a planner that combines a simulation of needle insertion 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.
Shortest path solution from start region (green segment) to target (circle) around obstacles (gray)
Maximizing probability of success using SMR shown in prostate ultrasound image
We have also developed planners that explicitly consider uncertainty in
needle motion in order to maximize the probability of avoiding collisions and successfully reaching the target.
The Stochastic Motion Roadmap (SMR) framework efficiently samples the state space, builds a "roadmap" through the tissues that encodes the system's motion uncertainty, formulates the planning problem as a Markov Decision Process (MDP), and determines a solution using dynamic programming to maximize the probability of successfully reaching the target. We applied this framework to compute steerable needle paths around obstacles to targets in tissues imaged using 2D slices. Explicitly accounting for uncertainty can lead to significantly different motion plans compared to traditional shortest paths, such as longer paths with greater clearance from obstacles in order to increase the probability of success.
This needle steering project is a collaborative effort involving research groups at The Johns Hopkins University, the University of California, Berkeley, Queens University, and Vanderbilt University.
Publications
Kyle B. Reed, Ann Majewicz, Vinutha Kallem, Ron Alterovitz, Ken Goldberg, Noah J. Cowan, Allison M. Okamura, "Robot-Assisted Needle Steering," IEEE Robotics and Automation Magazine, vol. 18, pp. 35-46, Dec. 2011.
(Download PDF)
Sachin Patil, Jur van den Berg, and Ron Alterovitz, "Motion Planning Under Uncertainty In Highly Deformable Environments," in Proc. Robotics: Science and Systems (RSS), June 2011.
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Edgar Lobaton, Jinghe Zhang, Sachin Patil, and Ron Alterovitz, "Planning Curvature-Constrained Paths to Multiple Goals Using Circle Sampling," in Proc. IEEE International Conference on Robotics and Automation (ICRA), May 2011, pp. 1463-1469.
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Jur van den Berg, Sachin Patil, Ron Alterovitz, Pieter Abbeel and Ken Goldberg, "LQG-Based Planning, Sensing, and Control of Steerable Needles, in Algorithmic Foundation of Robotics IX (WAFR 2010), D. Hsu et al. (Eds.), STAR vol. 68, Springer-Verlag, 2010, pp. 373-389.
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Sachin Patil and Ron Alterovitz, "Interactive Motion Planning for Steerable Needles in 3D Environments with Obstacles," in Proc. IEEE RAS/EMBS Int. Conf. Biomedical Robotics and Biomechatronics (BioRob), Sep. 2010, pp. 893-899.
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Vincent Duindam, Jijie Xu, Ron Alterovitz, Shankar Sastry, and Ken Goldberg,
"Three-dimensional Motion Planning Algorithms for Steerable Needles Using Inverse Kinematics,"
International Journal of Robotics Research, vol. 29, no. 7, pp. 789-800, June 2010.
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Nuttapong Chentanez, Ron Alterovitz, Daniel Ritchie, Jonha Cho, Kris Hauser, Ken Goldberg, Jonathan R. Shewchuk, and James F. O'Brien,
"Interactive Simulation of Surgical Needle Insertion and Steering,"
ACM Transactions on Graphics (Proc. SIGGRAPH), vol. 28, no. 3, pp. 88:1-88:10, Aug. 2009.
(Download PDF)(Video) (Image from article featured on back cover)
Ron Alterovitz, Michael Branicky, and Ken Goldberg,
"Motion Planning Under Uncertainty for Image-Guided Medical Needle Steering,"
International Journal of Robotics Research, vol. 27, no. 11-12, pp. 1361-1374, Nov. 2008.
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Kris Hauser, Ron Alterovitz, Nuttapong Chentanez, Allison Okamura, and Ken Goldberg,
"Feedback Control for Steering Needles Through 3D Deformable Tissue Using Helical Paths,"
in Proc. Robotics: Science and Systems, June 2009.
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Vincent Duindam, Jijie Xu, Ron Alterovitz, Shankar Sastry, and Ken Goldberg,
"3D Motion Planning Algorithms for Steerable Needles Using Inverse Kinematics,"
in Proc. WAFR 2008: The Eighth International Workshop on the Algorithmic Foundations of Robotics, Dec. 2008.
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Kyle B. Reed, Vinutha Kallem, Ron Alterovitz, Ken Goldberg, Allison M. Okamura, and Noah J. Cowan,
"Integrated Planning and Image-Guided Control for Planar Needle Steering,"
in Proc. IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Oct. 2008, pp. 819-824.
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Vincent Duindam, Ron Alterovitz, Shankar Sastry, and Ken Goldberg,
"Screw-Based Motion Planning for Bevel-Tip Flexible Needles in 3D Environments with Obstacles,"
in Proc. IEEE International Conference on Robotics and Automation (ICRA), May 2008, pp. 2483-2488.
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Ron Alterovitz, Thierry Siméon, and Ken Goldberg, "The Stochastic Motion Roadmap: A
Sampling Framework for Planning with Markov Motion Uncertainty,"
in Robotics: Science and Systems III (Proc. RSS 2007), W. Burgard et al. (Eds.), MIT Press, 2008, pp. 233-241.
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Ron Alterovitz, Michael Branicky, and Ken Goldberg, "Constant-Curvature Motion Planning
Under Uncertainty with Applications in Image-Guided Medical Needle Steering,"
in Algorithmic Foundation of Robotics VII (WAFR 2006), S. Akella et al. (Eds.), STAR vol. 47, Springer-Verlag, 2008, pp. 319-334.
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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.
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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.
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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.
