| Ron Alterovitz
Department of Computer Science,
University of North Carolina at Chapel Hill,
Chapel Hill, NC 27599-3175, USA,
Michael Branicky
Department of Electrical Engineering
and Computer Science,
Case Western Reserve University,
Cleveland, OH 44106, USA
and Ken Goldberg
Department of Industrial Engineering
and Operations Research,
Department of Electrical Engineering
and Computer Sciences,
University of California,
Berkeley, CA 94720, USA |
| We develop a new motion planning algorithm for a variant of a Dubins
car with binary left/right steering and apply it to steerable needles,
a new class of flexible bevel-tip medical needle that physicians can
steer through soft tissue to reach clinical targets inaccessible to traditional
stiff needles. Our method explicitly considers uncertainty in
needle motion due to patient differences and the difficulty in predicting
needle/tissue interaction. The planner computes optimal steering
actions to maximize the probability that the needle will reach the desired
target. Given a medical image with segmented obstacles and
target, our method formulates the planning problem as a Markov decision
process based on an efficient discretization of the state space,
models motion uncertainty using probability distributions and computes
optimal steering actions using dynamic programming. This approach
only requires parameters that can be directly extracted from
images, allows fast computation of the optimal needle entry point and
enables intra-operative optimal steering of the needle using the precomputed
dynamic programming look-up table. We apply the method to generate motion plans for steerable needles to reach targets inaccessible
to stiff needles, and we illustrate the importance of considering
uncertainty during motion plan optimization. |
