Semi-Automated Needle Steering in Biological Tissue Using an Ultrasound-Based Deflection Predictor
- 340 Downloads
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.
- 12.Minhas D. S., J. A. Engh, M. M. Fenske, and C. N. Riviere. Modeling of needle steering via duty-cycled spinning. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 2756–2759Google Scholar
- 13.Misra S., K. B. Reed, A. S. Douglas, K. T. Ramesh, and A. M. Okamura. Needle-tissue interaction forces for bevel-tip steerable needles. In: 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob, 2008, pp. 224–231Google Scholar
- 17.Podder T. K., D. P. Clark, D. Fuller, J. Sherman and et.al. Effects of velocity modulation during surgical needle insertion. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS. pp. 5766–5770Google Scholar
- 18.Reed K. B., V. Kallem, R. Alterovitz, K. Goldberg, A. M. Okamura, and N. J. Cowan. Integrated planning and image-guided control for planar needle steering. In: 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob, pp. 819–824Google Scholar
- 19.Roesthuis R. J., M. Abayazid and S. Misra. Mechanics-based model for predicting in-plane needle deflection with multiple bends. In: 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 69–74Google Scholar
- 20.Rossa C., N. Usmani, R. Sloboda and M. Tavakoli. A hand-held assistant for semi-automated percutaneous needle steering. IEEE Trans. Biomed. Eng. pp. 1–1, 2016Google Scholar
- 23.Vrooijink G. J., M. Abayazid, S. Patil, R. Alterovitz, and S. Misra. Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images. Int. J. Robot. Res., 2014Google Scholar
- 24.Waine M., C. Rossa, R. Sloboda, N. Usmani and M. Tavakoli. 3D needle shape estimation in TRUS-guided prostate brachytherapy using 2D ultrasound images. IEEE J. Biomed. Health Inf., pp. 1–1, 2015Google Scholar
- 25.Webster R., N. Cowan, G. Chirikjian, and A. Okamura. Nonholonomic modeling of needle steering. In: Experimental Robotics, Vol. IX. Berlin: Springer, 2006, pp. 35–44Google Scholar