TY - GEN
T1 - Feedback control for steering needles through 3D deformable tissue using helical paths
AU - Hauser, Kris
AU - Alterovitz, Ron
AU - Chentanez, Nuttapong
AU - Okamura, Allison
AU - Goldberg, Ken
PY - 2010
Y1 - 2010
N2 - Bevel-tip steerable needles are a promising new technology for improving accuracy and accessibility in minimally invasive medical procedures. As yet, 3D needle steering has not been demonstrated in the presence of tissue deformation and uncertainty, despite the application of progressively more sophisticated planning algorithms. This paper presents a feedback controller that steers a needle along 3D helical paths, and varies the helix radius to correct for perturbations. It achieves high accuracy for targets sufficiently far from the needle insertion point; this is counterintuitive because the system is highly underactuated and not locally controllable. The controller uses a model predictive control framework that chooses a needle twist rate such that the predicted helical trajectory minimizes the distance to the target. Fast branch and bound techniques enable execution at kilohertz rates on a 2GHz PC. We evaluate the controller under a variety of simulated perturbations, including imaging noise, needle deflections, and curvature estimation errors. We also test the controller in a 3D finite element simulator that incorporates deformation in the tissue as well as the needle. In deformable tissue examples, the controller reduced targeting error by up to 88% compared to open-loop execution.
AB - Bevel-tip steerable needles are a promising new technology for improving accuracy and accessibility in minimally invasive medical procedures. As yet, 3D needle steering has not been demonstrated in the presence of tissue deformation and uncertainty, despite the application of progressively more sophisticated planning algorithms. This paper presents a feedback controller that steers a needle along 3D helical paths, and varies the helix radius to correct for perturbations. It achieves high accuracy for targets sufficiently far from the needle insertion point; this is counterintuitive because the system is highly underactuated and not locally controllable. The controller uses a model predictive control framework that chooses a needle twist rate such that the predicted helical trajectory minimizes the distance to the target. Fast branch and bound techniques enable execution at kilohertz rates on a 2GHz PC. We evaluate the controller under a variety of simulated perturbations, including imaging noise, needle deflections, and curvature estimation errors. We also test the controller in a 3D finite element simulator that incorporates deformation in the tissue as well as the needle. In deformable tissue examples, the controller reduced targeting error by up to 88% compared to open-loop execution.
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U2 - 10.15607/rss.2009.v.037
DO - 10.15607/rss.2009.v.037
M3 - Conference contribution
AN - SCOPUS:84959540011
SN - 9780262514637
T3 - Robotics: Science and Systems
SP - 289
EP - 296
BT - Robotics
A2 - Trinkle, Jeff
A2 - Matsuoka, Yoky
A2 - Castellanos, Jose A.
PB - MIT Press Journals
T2 - International Conference on Robotics Science and Systems, RSS 2009
Y2 - 28 June 2009 through 1 July 2009
ER -