15 March 2013 Experimental assessment of error in an electromagnetically-tracked ultrasound-guided needle navigation system
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Purpose: Electromagnetic (EM)-tracked ultrasound (US)-guided needle navigation systems have potential use in spinal interventions; however, an assessment of the accuracy of these systems is required. Analysis of these systems involves examining the overall error of the system and the error of its components. The purpose of this study is to estimate the error components in an EM-tracked US-guided needle navigation system, and to determine the relationships between them, specifically for evaluation of US probe calibration. Methods: The main parts of the experimental setup are the US probe, the tracker, and the needle. The system error is examined by imaging the tracked needle with the US probe. The positional tracking error is tested for multiple needle, probe and reference sensors using a 7×9 grid with 4 cm spacing between points. Needle calibration error is evaluated by pivot calibration. An upper bound for the probe calibration error is then estimated using a series of transformations between the tracker and the needle tip position. Results: For all experiments, the mean error and its standard deviation increase as a function of distance from the tracker. The upper-bound of the US probe calibration error is estimated to be 1.81 mm. Conclusion: Operating distance has significant impact on component error, and the optimal operating distance for the presented setup has been shown. Although US probe calibration error cannot be measured directly, its upper-bound has been estimated by assessing the errors in other components of the system.
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Irene Ayukawa, Irene Ayukawa, Tamas Ungi, Tamas Ungi, Keyvan Hashtrudi-Zaad, Keyvan Hashtrudi-Zaad, Gabor Fichtinger, Gabor Fichtinger, Parvin Mousavi, Parvin Mousavi, } "Experimental assessment of error in an electromagnetically-tracked ultrasound-guided needle navigation system", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867124 (15 March 2013); doi: 10.1117/12.2007160; https://doi.org/10.1117/12.2007160

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