This paper describes a framework of algorithms for the active localization and tracking of flexible needles andtargetsduringimage-guidedpercutaneousinterventions. TheneedleandtargetconfigurationsaretrackedbyBayesian filters employing models of the needle and target motions and measurements of the current system state obtained from an intra-operative imaging system which is controlled by an entropy-minimizing active localization algorithm. Versions of the system were built using particle and unscented Kalman filters and their performance was measured using both simulations and hardware experiments with real magnetic resonance imaging data of needle insertions into gel phantoms. Performance of the localization algorithms is given in terms of accuracy of the predictions and computational efficiency is discussed.

EB00000003467K

Available

• Active localization and tracking of needle and target in robotic image-guided intervention systems