Magnetic Needle Steering Model Identification Using Expectation-Maximization

Deep Brain Stimulation is used to treat Parkinson's disease and other neurological disorders by implanting an electrode into the brain via a straight-path needle insertion. Enabling course correction and curved trajectories by using a steerable needle has the potential to improve operative outcomes. In this work, a physically motivated dynamic model for an actively steered magnetic-tipped needle is derived. Process and measurement noise covariances and model parameter curvature gain are identified using an expectation-maximization (EM) algorithm. Parameter convergence and accuracy are evaluated, and an RMS position trajectory accuracy of 0. 81 mm is calculated for expected conditions. The EM algorithm converged for expected parameter variations in simulation, which supports the EM implementation's use in identifying the parameters of the model in a physical system.