Semi-Automated Needle Steering in Biological Tissue Using an Ultrasound-Based Deflection Predictor
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The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm.
KeywordsMedical robotics Needle steering Motion planning Homotopy analysis method
This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under grant CHRP 446520, the Canadian Institutes of Health Research (CIHR) under grant CPG 127768 and the Alberta Innovates - Health Solutions (AIHS) under grant CRIO 201201232. The authors would like to thank Dr. Muhammad Faisal Jamaluddin who worked closely with us in conducting the evaluation experiments and helping to analyze our research.
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